Kaggle Home Credit Default Risk Competition

https://www.kaggle.com/c/home-credit-default-risk

This competition is sponsored by Home Credit, whose mission is to provide a positive and safe borrowing experience to groups of people that traditional, mainstream banks and financial institutions typically refuse to serve.

In order to make lending decisions on applicants from this demographic, Home Credit needs an algorithm that will take as inputs various financial and personal information originally taken from a loan applicant's profile, and then compute a probability that the applicant will have trouble paying back the loan. This probability will be in the range [0.0, 1.0], where 1.0 represents a 100% certainty that the applicant will have repayment difficulties and 0.0 indicates that there is zero chance that the applicant will ever miss any payments. The algorithm will be tested and ranked on Kaggle based on a set predictions it makes for 48,744 individuals who previously borrowed from Home Credit.

Solution algorithms will be trained on a set of datapoints from 307,511 previous Home Credit borrowers. It is imperative that some portion, say 20%, of the training set is set aside to serve as a validation set. Alternatively, an algorithm such as K-Fold Cross Validation could be used.

To submit a solution on Kaggle, a CSV file must be produced that contains one header row, and 48,744 prediction rows, where each prediction row contains both a user ID, the SKI_ID_CURR column, and the probability, the TARGET column, of that user having repayment defficulties. The file must be formatted as follows:

SK_ID_CURR,TARGET
100001,0.1
100005,0.9
100013,0.2
etc.

Home Credit knows which borrowers in the test set were delinquent, and which ones never made a late payment. A good algorithm will need to predict a high probability of delinquent repayment for the majority of borrowers who did in fact make late payments (those whose TARGET value is 1 in the main table in the dataset). This algorithm will also need to predict a low probability of delinquent repayment for the majority of borrowers who never made a late payment (those whose TARGET value is 0 in the main table in the dataset).

I. Data Exploration

In [47]:
# Import libraries necessary for this project.
import numpy as np
import pandas as pd
import matplotlib as mpl
import matplotlib.pyplot as plt
import seaborn as sns

# Import supplementary visualizations code visuals.py
import visuals as vs

# Display matplotlib plots inline in this notebook.
%matplotlib inline
# Make plots display well on retina displays
%config InlineBackend.figure_format = 'retina'
# Set dpi of plots displayed inline
mpl.rcParams['figure.dpi'] = 300
# Configure style of plots
plt.style.use('fivethirtyeight')
# Make plots smaller
sns.set_context('paper')  

# Allows the use of display() for dataframes.
from IPython.display import display
# Have all columns appear when dataframes are displayed.
pd.set_option('display.max_columns', None) 
# Have 100 rows appear when a dataframe is displayed
pd.set_option('display.max_rows', 500)
# Display dimensions whenever a dataframe is printed out.
pd.set_option('display.show_dimensions', True)

Data Description

From https://www.kaggle.com/c/home-credit-default-risk/data:

  1. application_{train|test}.csv
    • This is the main table, broken into two files for Train (with TARGET) and Test (without TARGET).
    • Static data for all applications. One row represents one loan in our data sample.

  2. bureau.csv
    • All client's previous credits provided by other financial institutions that were reported to Credit Bureau (for clients who have a loan in our sample).
    • For every loan in our sample, there are as many rows as number of credits the client had in Credit Bureau before the application date.

  3. bureau_balance.csv

    • Monthly balances of previous credits in Credit Bureau.
    • This table has one row for each month of history of every previous credit reported to Credit Bureau – i.e the table has (#loans in sample # of relative previous credits # of months where we have some history observable for the previous credits) rows.

  4. previous_application.csv

    • All previous applications for Home Credit loans of clients who have loans in our sample.
    • There is one row for each previous application related to loans in our data sample.

  5. POS_CASH_balance.csv

    • Monthly balance snapshots of previous POS (point of sales) and cash loans that the applicant had with Home Credit.
    • This table has one row for each month of history of every previous credit in Home Credit (consumer credit and cash loans) related to loans in our sample – i.e. the table has (#loans in sample # of relative previous credits # of months in which we have some history observable for the previous credits) rows.

  6. installments_payments.csv

    • Repayment history for the previously disbursed credits in Home Credit related to the loans in our sample.
    • There is a) one row for every payment that was made plus b) one row each for missed payment.
    • One row is equivalent to one payment of one installment OR one installment corresponding to one payment of one previous Home Credit credit related to loans in our sample.

  7. credit_card_balance.csv

    • Monthly balance snapshots of previous credit cards that the applicant has with Home Credit.
    • This table has one row for each month of history of every previous credit in Home Credit (consumer credit and cash loans) related to loans in our sample – i.e. the table has (#loans in sample # of relative previous credit cards # of months where we have some history observable for the previous credit card) rows.

Data Table Diagram

In [2]:
# Load the main data tables
application_train_data = pd.read_csv("data/application_train.csv")
application_test_data = pd.read_csv("data/application_test.csv")
In [71]:
# Load the Bureau data table
bureau_data = pd.read_csv("data/bureau.csv")
In [5]:
# Load all other data tables
bureau_balance_data = pd.read_csv("data/bureau_balance.csv")
previous_application_data = pd.read_csv("data/previous_application.csv")
POS_CASH_balance_data = pd.read_csv("data/POS_CASH_balance.csv")
installments_payments_data = pd.read_csv("data/installments_payments.csv")
credit_card_balance_data = pd.read_csv("data/credit_card_balance.csv")

1. Main Data Table (application_{train|test}.csv)

In [3]:
# Total number of entries in training group
print("Total number of entries in training group: {}".format(application_train_data.shape[0]))
Total number of entries in training group: 307511
In [4]:
# Total number of entries in test group
print("Total number of entries in test group: {}".format(application_test_data.shape[0]))
Total number of entries in test group: 48744
In [5]:
# Total number of features in the main (application) data table
print("Total number of features in main (application) data table: {}".format(application_train_data.shape[1]))
Total number of features in main (application) data table: 122

The first two features in the main data table training group, SK_ID_CURR and TARGET, represent the borrower's ID number and target data (whether or not they made at least one late payment), respectively.

There are therefore 120 features in the main data table that can be used to predict a borrowers' targets.

In [183]:
# Display the first 500 records
display(application_train_data.head(n=500))
SK_ID_CURR TARGET NAME_CONTRACT_TYPE CODE_GENDER FLAG_OWN_CAR FLAG_OWN_REALTY CNT_CHILDREN AMT_INCOME_TOTAL AMT_CREDIT AMT_ANNUITY AMT_GOODS_PRICE NAME_TYPE_SUITE NAME_INCOME_TYPE NAME_EDUCATION_TYPE NAME_FAMILY_STATUS NAME_HOUSING_TYPE REGION_POPULATION_RELATIVE DAYS_BIRTH DAYS_EMPLOYED DAYS_REGISTRATION DAYS_ID_PUBLISH OWN_CAR_AGE FLAG_MOBIL FLAG_EMP_PHONE FLAG_WORK_PHONE FLAG_CONT_MOBILE FLAG_PHONE FLAG_EMAIL OCCUPATION_TYPE CNT_FAM_MEMBERS REGION_RATING_CLIENT REGION_RATING_CLIENT_W_CITY WEEKDAY_APPR_PROCESS_START HOUR_APPR_PROCESS_START REG_REGION_NOT_LIVE_REGION REG_REGION_NOT_WORK_REGION LIVE_REGION_NOT_WORK_REGION REG_CITY_NOT_LIVE_CITY REG_CITY_NOT_WORK_CITY LIVE_CITY_NOT_WORK_CITY ORGANIZATION_TYPE EXT_SOURCE_1 EXT_SOURCE_2 EXT_SOURCE_3 APARTMENTS_AVG BASEMENTAREA_AVG YEARS_BEGINEXPLUATATION_AVG YEARS_BUILD_AVG COMMONAREA_AVG ELEVATORS_AVG ENTRANCES_AVG FLOORSMAX_AVG FLOORSMIN_AVG LANDAREA_AVG LIVINGAPARTMENTS_AVG LIVINGAREA_AVG NONLIVINGAPARTMENTS_AVG NONLIVINGAREA_AVG APARTMENTS_MODE BASEMENTAREA_MODE YEARS_BEGINEXPLUATATION_MODE YEARS_BUILD_MODE COMMONAREA_MODE ELEVATORS_MODE ENTRANCES_MODE FLOORSMAX_MODE FLOORSMIN_MODE LANDAREA_MODE LIVINGAPARTMENTS_MODE LIVINGAREA_MODE NONLIVINGAPARTMENTS_MODE NONLIVINGAREA_MODE APARTMENTS_MEDI BASEMENTAREA_MEDI YEARS_BEGINEXPLUATATION_MEDI YEARS_BUILD_MEDI COMMONAREA_MEDI ELEVATORS_MEDI ENTRANCES_MEDI FLOORSMAX_MEDI FLOORSMIN_MEDI LANDAREA_MEDI LIVINGAPARTMENTS_MEDI LIVINGAREA_MEDI NONLIVINGAPARTMENTS_MEDI NONLIVINGAREA_MEDI FONDKAPREMONT_MODE HOUSETYPE_MODE TOTALAREA_MODE WALLSMATERIAL_MODE EMERGENCYSTATE_MODE OBS_30_CNT_SOCIAL_CIRCLE DEF_30_CNT_SOCIAL_CIRCLE OBS_60_CNT_SOCIAL_CIRCLE DEF_60_CNT_SOCIAL_CIRCLE DAYS_LAST_PHONE_CHANGE FLAG_DOCUMENT_2 FLAG_DOCUMENT_3 FLAG_DOCUMENT_4 FLAG_DOCUMENT_5 FLAG_DOCUMENT_6 FLAG_DOCUMENT_7 FLAG_DOCUMENT_8 FLAG_DOCUMENT_9 FLAG_DOCUMENT_10 FLAG_DOCUMENT_11 FLAG_DOCUMENT_12 FLAG_DOCUMENT_13 FLAG_DOCUMENT_14 FLAG_DOCUMENT_15 FLAG_DOCUMENT_16 FLAG_DOCUMENT_17 FLAG_DOCUMENT_18 FLAG_DOCUMENT_19 FLAG_DOCUMENT_20 FLAG_DOCUMENT_21 AMT_REQ_CREDIT_BUREAU_HOUR AMT_REQ_CREDIT_BUREAU_DAY AMT_REQ_CREDIT_BUREAU_WEEK AMT_REQ_CREDIT_BUREAU_MON AMT_REQ_CREDIT_BUREAU_QRT AMT_REQ_CREDIT_BUREAU_YEAR
0 100002 1 Cash loans M N Y 0 202500.000 406597.5 24700.5 351000.0 Unaccompanied Working Secondary / secondary special Single / not married House / apartment 0.018801 -9461 -637 -3648.0 -2120 NaN 1 1 0 1 1 0 Laborers 1.0 2 2 WEDNESDAY 10 0 0 0 0 0 0 Business Entity Type 3 0.083037 0.262949 0.139376 0.0247 0.0369 0.9722 0.6192 0.0143 0.0000 0.0690 0.0833 0.1250 0.0369 0.0202 0.0190 0.0000 0.0000 0.0252 0.0383 0.9722 0.6341 0.0144 0.0000 0.0690 0.0833 0.1250 0.0377 0.0220 0.0198 0.0000 0.0000 0.0250 0.0369 0.9722 0.6243 0.0144 0.00 0.0690 0.0833 0.1250 0.0375 0.0205 0.0193 0.0000 0.0000 reg oper account block of flats 0.0149 Stone, brick No 2.0 2.0 2.0 2.0 -1134.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 1.0
1 100003 0 Cash loans F N N 0 270000.000 1293502.5 35698.5 1129500.0 Family State servant Higher education Married House / apartment 0.003541 -16765 -1188 -1186.0 -291 NaN 1 1 0 1 1 0 Core staff 2.0 1 1 MONDAY 11 0 0 0 0 0 0 School 0.311267 0.622246 NaN 0.0959 0.0529 0.9851 0.7960 0.0605 0.0800 0.0345 0.2917 0.3333 0.0130 0.0773 0.0549 0.0039 0.0098 0.0924 0.0538 0.9851 0.8040 0.0497 0.0806 0.0345 0.2917 0.3333 0.0128 0.0790 0.0554 0.0000 0.0000 0.0968 0.0529 0.9851 0.7987 0.0608 0.08 0.0345 0.2917 0.3333 0.0132 0.0787 0.0558 0.0039 0.0100 reg oper account block of flats 0.0714 Block No 1.0 0.0 1.0 0.0 -828.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 0.0
2 100004 0 Revolving loans M Y Y 0 67500.000 135000.0 6750.0 135000.0 Unaccompanied Working Secondary / secondary special Single / not married House / apartment 0.010032 -19046 -225 -4260.0 -2531 26.0 1 1 1 1 1 0 Laborers 1.0 2 2 MONDAY 9 0 0 0 0 0 0 Government NaN 0.555912 0.729567 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 0.0 0.0 0.0 0.0 -815.0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 0.0
3 100006 0 Cash loans F N Y 0 135000.000 312682.5 29686.5 297000.0 Unaccompanied Working Secondary / secondary special Civil marriage House / apartment 0.008019 -19005 -3039 -9833.0 -2437 NaN 1 1 0 1 0 0 Laborers 2.0 2 2 WEDNESDAY 17 0 0 0 0 0 0 Business Entity Type 3 NaN 0.650442 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 2.0 0.0 2.0 0.0 -617.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NaN NaN NaN NaN NaN NaN
4 100007 0 Cash loans M N Y 0 121500.000 513000.0 21865.5 513000.0 Unaccompanied Working Secondary / secondary special Single / not married House / apartment 0.028663 -19932 -3038 -4311.0 -3458 NaN 1 1 0 1 0 0 Core staff 1.0 2 2 THURSDAY 11 0 0 0 0 1 1 Religion NaN 0.322738 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 0.0 0.0 0.0 0.0 -1106.0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 0.0
5 100008 0 Cash loans M N Y 0 99000.000 490495.5 27517.5 454500.0 Spouse, partner State servant Secondary / secondary special Married House / apartment 0.035792 -16941 -1588 -4970.0 -477 NaN 1 1 1 1 1 0 Laborers 2.0 2 2 WEDNESDAY 16 0 0 0 0 0 0 Other NaN 0.354225 0.621226 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 0.0 0.0 0.0 0.0 -2536.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 1.0 1.0
6 100009 0 Cash loans F Y Y 1 171000.000 1560726.0 41301.0 1395000.0 Unaccompanied Commercial associate Higher education Married House / apartment 0.035792 -13778 -3130 -1213.0 -619 17.0 1 1 0 1 1 0 Accountants 3.0 2 2 SUNDAY 16 0 0 0 0 0 0 Business Entity Type 3 0.774761 0.724000 0.492060 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 1.0 0.0 1.0 0.0 -1562.0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0.0 0.0 0.0 1.0 1.0 2.0
7 100010 0 Cash loans M Y Y 0 360000.000 1530000.0 42075.0 1530000.0 Unaccompanied State servant Higher education Married House / apartment 0.003122 -18850 -449 -4597.0 -2379 8.0 1 1 1 1 0 0 Managers 2.0 3 3 MONDAY 16 0 0 0 0 1 1 Other NaN 0.714279 0.540654 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 2.0 0.0 2.0 0.0 -1070.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 0.0
8 100011 0 Cash loans F N Y 0 112500.000 1019610.0 33826.5 913500.0 Children Pensioner Secondary / secondary special Married House / apartment 0.018634 -20099 365243 -7427.0 -3514 NaN 1 0 0 1 0 0 NaN 2.0 2 2 WEDNESDAY 14 0 0 0 0 0 0 XNA 0.587334 0.205747 0.751724 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 1.0 0.0 1.0 0.0 0.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 1.0
9 100012 0 Revolving loans M N Y 0 135000.000 405000.0 20250.0 405000.0 Unaccompanied Working Secondary / secondary special Single / not married House / apartment 0.019689 -14469 -2019 -14437.0 -3992 NaN 1 1 0 1 0 0 Laborers 1.0 2 2 THURSDAY 8 0 0 0 0 0 0 Electricity NaN 0.746644 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 2.0 0.0 2.0 0.0 -1673.0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NaN NaN NaN NaN NaN NaN
10 100014 0 Cash loans F N Y 1 112500.000 652500.0 21177.0 652500.0 Unaccompanied Working Higher education Married House / apartment 0.022800 -10197 -679 -4427.0 -738 NaN 1 1 0 1 0 0 Core staff 3.0 2 2 SATURDAY 15 0 0 0 0 0 0 Medicine 0.319760 0.651862 0.363945 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 0.0 0.0 0.0 0.0 -844.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 1.0 0.0 0.0
11 100015 0 Cash loans F N Y 0 38419.155 148365.0 10678.5 135000.0 Children Pensioner Secondary / secondary special Married House / apartment 0.015221 -20417 365243 -5246.0 -2512 NaN 1 0 0 1 1 0 NaN 2.0 2 2 FRIDAY 7 0 0 0 0 0 0 XNA 0.722044 0.555183 0.652897 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 0.0 0.0 0.0 0.0 -2396.0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 2.0
12 100016 0 Cash loans F N Y 0 67500.000 80865.0 5881.5 67500.0 Unaccompanied Working Secondary / secondary special Married House / apartment 0.031329 -13439 -2717 -311.0 -3227 NaN 1 1 1 1 1 0 Laborers 2.0 2 2 FRIDAY 10 0 0 0 0 0 0 Business Entity Type 2 0.464831 0.715042 0.176653 0.0825 NaN 0.9811 NaN NaN 0.0000 0.2069 0.1667 NaN 0.0135 NaN 0.0778 NaN 0.0000 0.0840 NaN 0.9811 NaN NaN 0.0000 0.2069 0.1667 NaN 0.0138 NaN 0.0810 NaN 0.0000 0.0833 NaN 0.9811 NaN NaN 0.00 0.2069 0.1667 NaN 0.0137 NaN 0.0792 NaN 0.0000 reg oper account block of flats 0.0612 NaN No 0.0 0.0 0.0 0.0 -2370.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 1.0 0.0 0.0
13 100017 0 Cash loans M Y N 1 225000.000 918468.0 28966.5 697500.0 Unaccompanied Working Secondary / secondary special Married House / apartment 0.016612 -14086 -3028 -643.0 -4911 23.0 1 1 0 1 0 0 Drivers 3.0 2 2 THURSDAY 13 0 0 0 0 0 0 Self-employed NaN 0.566907 0.770087 0.1474 0.0973 0.9806 0.7348 0.0582 0.1600 0.1379 0.3333 0.3750 0.0931 0.1202 0.1397 0.0000 0.0000 0.1502 0.1010 0.9806 0.7452 0.0587 0.1611 0.1379 0.3333 0.3750 0.0952 0.1313 0.1456 0.0000 0.0000 0.1489 0.0973 0.9806 0.7383 0.0585 0.16 0.1379 0.3333 0.3750 0.0947 0.1223 0.1422 0.0000 0.0000 reg oper account block of flats 0.1417 Panel No 0.0 0.0 0.0 0.0 -4.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 1.0
14 100018 0 Cash loans F N Y 0 189000.000 773680.5 32778.0 679500.0 Unaccompanied Working Secondary / secondary special Married House / apartment 0.010006 -14583 -203 -615.0 -2056 NaN 1 1 0 1 0 0 Laborers 2.0 2 1 MONDAY 9 0 0 0 0 0 0 Transport: type 2 0.721940 0.642656 NaN 0.3495 0.1335 0.9985 0.9796 0.1143 0.4000 0.1724 0.6667 0.7083 0.1758 0.2849 0.3774 0.0193 0.1001 0.3561 0.1386 0.9985 0.9804 0.1153 0.4028 0.1724 0.6667 0.7083 0.1798 0.3113 0.3932 0.0195 0.1060 0.3529 0.1335 0.9985 0.9799 0.1150 0.40 0.1724 0.6667 0.7083 0.1789 0.2899 0.3842 0.0194 0.1022 reg oper account block of flats 0.3811 Panel No 0.0 0.0 0.0 0.0 -188.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NaN NaN NaN NaN NaN NaN
15 100019 0 Cash loans M Y Y 0 157500.000 299772.0 20160.0 247500.0 Family Working Secondary / secondary special Single / not married Rented apartment 0.020713 -8728 -1157 -3494.0 -1368 17.0 1 1 0 1 0 0 Laborers 1.0 3 3 SATURDAY 6 0 0 0 1 1 0 Business Entity Type 2 0.115634 0.346634 0.678568 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 0.0 0.0 0.0 0.0 -925.0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 1.0
16 100020 0 Cash loans M N N 0 108000.000 509602.5 26149.5 387000.0 Unaccompanied Working Secondary / secondary special Married House / apartment 0.018634 -12931 -1317 -6392.0 -3866 NaN 1 1 0 1 0 0 Drivers 2.0 2 2 THURSDAY 12 0 0 0 1 1 0 Government NaN 0.236378 0.062103 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 0.0 0.0 0.0 0.0 -3.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 1.0 0.0
17 100021 0 Revolving loans F N Y 1 81000.000 270000.0 13500.0 270000.0 Unaccompanied Working Secondary / secondary special Married House / apartment 0.010966 -9776 -191 -4143.0 -2427 NaN 1 1 0 1 0 0 Laborers 3.0 2 2 MONDAY 10 0 0 0 1 1 0 Construction NaN 0.683513 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 4.0 0.0 4.0 0.0 -2811.0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NaN NaN NaN NaN NaN NaN
18 100022 0 Revolving loans F N Y 0 112500.000 157500.0 7875.0 157500.0 Other_A Working Secondary / secondary special Widow House / apartment 0.046220 -17718 -7804 -8751.0 -1259 NaN 1 1 0 1 1 0 Laborers 1.0 1 1 FRIDAY 13 0 0 0 0 0 0 Housing NaN 0.706428 0.556727 0.0278 0.0617 0.9881 0.8368 0.0018 0.0000 0.1034 0.0833 0.1250 0.0279 0.0227 0.0290 0.0000 0.0000 0.0284 0.0640 0.9881 0.8432 0.0018 0.0000 0.1034 0.0833 0.1250 0.0286 0.0248 0.0302 0.0000 0.0000 0.0281 0.0617 0.9881 0.8390 0.0018 0.00 0.1034 0.0833 0.1250 0.0284 0.0231 0.0295 0.0000 0.0000 reg oper account block of flats 0.0238 Stone, brick No 8.0 0.0 8.0 0.0 -239.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 0.0
19 100023 0 Cash loans F N Y 1 90000.000 544491.0 17563.5 454500.0 Unaccompanied State servant Higher education Single / not married House / apartment 0.015221 -11348 -2038 -1021.0 -3964 NaN 1 1 1 1 1 0 Core staff 2.0 2 2 MONDAY 12 0 0 0 0 0 0 Kindergarten NaN 0.586617 0.477649 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 0.0 0.0 0.0 0.0 -1850.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 2.0
20 100024 0 Revolving loans M Y Y 0 135000.000 427500.0 21375.0 427500.0 Unaccompanied Working Secondary / secondary special Married House / apartment 0.015221 -18252 -4286 -298.0 -1800 7.0 1 1 0 1 0 0 Laborers 2.0 2 2 FRIDAY 13 0 0 0 0 0 0 Self-employed 0.565655 0.113375 NaN 0.0722 0.0801 0.9781 0.7008 NaN 0.0000 0.1379 0.1667 0.0417 0.0534 0.0588 0.0619 0.0000 0.0000 0.0735 0.0831 0.9782 0.7125 NaN 0.0000 0.1379 0.1667 0.0417 0.0546 0.0643 0.0645 0.0000 0.0000 0.0729 0.0801 0.9781 0.7048 NaN 0.00 0.1379 0.1667 0.0417 0.0543 0.0599 0.0630 0.0000 0.0000 reg oper account block of flats 0.0530 Stone, brick No 0.0 0.0 0.0 0.0 -296.0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NaN NaN NaN NaN NaN NaN
21 100025 0 Cash loans F Y Y 1 202500.000 1132573.5 37561.5 927000.0 Unaccompanied Commercial associate Secondary / secondary special Married House / apartment 0.025164 -14815 -1652 -2299.0 -2299 14.0 1 1 0 1 0 0 Sales staff 3.0 2 2 MONDAY 9 0 0 0 0 0 0 Trade: type 7 0.437709 0.233767 0.542445 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 0.0 0.0 0.0 0.0 0.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 1.0 4.0
22 100026 0 Cash loans F N N 1 450000.000 497520.0 32521.5 450000.0 Unaccompanied Working Secondary / secondary special Married Rented apartment 0.020713 -11146 -4306 -114.0 -2518 NaN 1 1 0 1 0 0 Sales staff 3.0 3 2 THURSDAY 6 0 0 0 0 0 0 Self-employed NaN 0.457143 0.358951 0.0907 0.0795 0.9786 0.7076 0.0120 0.0000 0.2069 0.1667 0.2083 0.0898 0.0723 0.0873 0.0077 0.0044 0.0924 0.0825 0.9786 0.7190 0.0121 0.0000 0.2069 0.1667 0.2083 0.0918 0.0790 0.0910 0.0078 0.0047 0.0916 0.0795 0.9786 0.7115 0.0120 0.00 0.2069 0.1667 0.2083 0.0913 0.0735 0.0889 0.0078 0.0045 reg oper account block of flats 0.0696 Panel No 0.0 0.0 0.0 0.0 -468.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 5.0
23 100027 0 Cash loans F N Y 0 83250.000 239850.0 23850.0 225000.0 Unaccompanied Pensioner Secondary / secondary special Married House / apartment 0.006296 -24827 365243 -9012.0 -3684 NaN 1 0 0 1 1 0 NaN 2.0 3 3 FRIDAY 12 0 0 0 0 0 0 XNA NaN 0.624305 0.669057 0.1443 0.0848 0.9876 0.8300 0.1064 0.1400 0.1207 0.3750 0.4167 0.2371 0.1173 0.1484 0.0019 0.0007 0.1261 0.0754 0.9876 0.8367 0.0000 0.1208 0.1034 0.3750 0.4167 0.2425 0.1102 0.0923 0.0000 0.0000 0.1457 0.0848 0.9876 0.8323 0.1071 0.14 0.1207 0.3750 0.4167 0.2412 0.1193 0.1510 0.0019 0.0007 org spec account block of flats 0.1640 Mixed No 0.0 0.0 0.0 0.0 -795.0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 3.0
24 100029 0 Cash loans M Y N 2 135000.000 247500.0 12703.5 247500.0 Unaccompanied Working Secondary / secondary special Married House / apartment 0.026392 -11286 -746 -108.0 -3729 7.0 1 1 0 1 0 0 Drivers 4.0 2 2 THURSDAY 14 0 0 0 0 1 1 Business Entity Type 3 NaN 0.786179 0.565608 0.1433 0.1455 0.9861 0.8096 0.0212 0.0000 0.3103 0.1667 0.2083 0.0861 0.1168 0.1217 0.0000 0.0043 0.1460 0.1509 0.9861 0.8171 0.0214 0.0000 0.3103 0.1667 0.2083 0.0881 0.1276 0.1268 0.0000 0.0045 0.1447 0.1455 0.9861 0.8121 0.0214 0.00 0.3103 0.1667 0.2083 0.0876 0.1189 0.1239 0.0000 0.0044 reg oper account block of flats 0.0967 Panel No 1.0 1.0 1.0 0.0 -4.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 0.0
25 100030 0 Cash loans F N Y 0 90000.000 225000.0 11074.5 225000.0 Unaccompanied Working Secondary / secondary special Married House / apartment 0.028663 -19334 -3494 -2419.0 -2893 NaN 1 1 0 1 1 0 Cleaning staff 2.0 2 2 SATURDAY 8 0 0 0 0 0 0 Business Entity Type 3 0.561948 0.651406 0.461482 0.0722 0.0147 0.9781 0.7008 0.0010 0.0000 0.1379 0.1667 0.0417 0.0498 0.0588 0.0670 0.0000 0.0000 0.0735 0.0153 0.9782 0.7125 0.0010 0.0000 0.1379 0.1667 0.0417 0.0509 0.0643 0.0698 0.0000 0.0000 0.0729 0.0147 0.9781 0.7048 0.0010 0.00 0.1379 0.1667 0.0417 0.0506 0.0599 0.0682 0.0000 0.0000 reg oper account block of flats 0.0533 Stone, brick No 0.0 0.0 0.0 0.0 0.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 0.0
26 100031 1 Cash loans F N Y 0 112500.000 979992.0 27076.5 702000.0 Unaccompanied Working Secondary / secondary special Widow House / apartment 0.018029 -18724 -2628 -6573.0 -1827 NaN 1 1 0 1 0 0 Cooking staff 1.0 3 2 MONDAY 9 0 0 0 0 0 0 Business Entity Type 3 NaN 0.548477 0.190706 0.0165 0.0089 0.9732 NaN NaN 0.0000 0.0690 0.0417 NaN 0.0265 NaN 0.0094 NaN 0.0000 0.0168 0.0092 0.9732 NaN NaN 0.0000 0.0690 0.0417 NaN 0.0271 NaN 0.0083 NaN 0.0000 0.0167 0.0089 0.9732 NaN NaN 0.00 0.0690 0.0417 NaN 0.0270 NaN 0.0096 NaN 0.0000 NaN block of flats 0.0085 Wooden Yes 10.0 1.0 10.0 0.0 -161.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0.0 0.0 0.0 0.0 2.0 2.0
27 100032 0 Cash loans M N Y 1 112500.000 327024.0 23827.5 270000.0 Family Working Secondary / secondary special Married House / apartment 0.019101 -15948 -1234 -5782.0 -3153 NaN 1 1 0 1 0 0 Laborers 3.0 2 2 SATURDAY 10 0 0 0 0 1 1 Industry: type 11 NaN 0.541124 0.659406 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 2.0 0.0 2.0 0.0 -2.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 0.0
28 100033 0 Cash loans M Y Y 0 270000.000 790830.0 57676.5 675000.0 Unaccompanied State servant Higher education Single / not married House / apartment 0.046220 -9994 -1796 -4668.0 -2661 1.0 1 1 0 1 0 1 NaN 1.0 1 1 WEDNESDAY 11 0 1 1 0 0 0 Military 0.600396 0.685011 0.524496 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 0.0 0.0 0.0 0.0 -849.0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 1.0 0.0 1.0
29 100034 0 Revolving loans M N Y 0 90000.000 180000.0 9000.0 180000.0 Unaccompanied Working Higher education Single / not married With parents 0.030755 -10341 -1010 -4799.0 -3015 NaN 1 1 0 1 0 0 Laborers 1.0 2 2 TUESDAY 16 0 0 0 0 0 0 Business Entity Type 3 0.297914 0.502779 NaN 0.1505 0.0838 0.9831 0.7688 0.0188 0.1600 0.1379 0.3333 0.3750 0.0872 0.1210 0.1412 0.0077 0.0061 0.1534 0.0870 0.9831 0.7779 0.0190 0.1611 0.1379 0.3333 0.3750 0.0892 0.1322 0.1471 0.0078 0.0065 0.1520 0.0838 0.9831 0.7719 0.0189 0.16 0.1379 0.3333 0.3750 0.0887 0.1231 0.1437 0.0078 0.0063 reg oper account block of flats 0.1227 Panel No 0.0 0.0 0.0 0.0 -599.0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NaN NaN NaN NaN NaN NaN
30 100035 0 Cash loans F N Y 0 292500.000 665892.0 24592.5 477000.0 Unaccompanied Commercial associate Secondary / secondary special Civil marriage House / apartment 0.025164 -15280 -2668 -5266.0 -3787 NaN 1 1 0 1 0 1 NaN 2.0 2 2 WEDNESDAY 13 0 0 0 0 0 0 Business Entity Type 3 NaN 0.479987 0.410103 0.0124 NaN 0.9697 NaN NaN 0.0000 0.0690 0.0417 NaN NaN NaN 0.0149 NaN 0.0000 0.0126 NaN 0.9697 NaN NaN 0.0000 0.0690 0.0417 NaN NaN NaN 0.0155 NaN 0.0000 0.0125 NaN 0.9697 NaN NaN 0.00 0.0690 0.0417 NaN NaN NaN 0.0152 NaN 0.0000 NaN block of flats 0.0117 Others No 0.0 0.0 0.0 0.0 -1634.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 1.0 0.0 5.0
31 100036 0 Cash loans F N Y 0 112500.000 512064.0 25033.5 360000.0 Family Working Secondary / secondary special Civil marriage House / apartment 0.008575 -11144 -1104 -7846.0 -2904 NaN 1 1 0 1 0 0 Private service staff 2.0 2 2 TUESDAY 12 0 0 0 0 0 0 Services 0.274422 0.627300 NaN 0.3670 0.3751 0.9901 NaN NaN 0.2800 0.4828 0.3750 NaN 0.1569 NaN 0.2574 NaN NaN 0.3739 0.3893 0.9901 NaN NaN 0.2820 0.4828 0.3750 NaN 0.1604 NaN 0.2682 NaN NaN 0.3706 0.3751 0.9901 NaN NaN 0.28 0.4828 0.3750 NaN 0.1596 NaN 0.2620 NaN NaN NaN block of flats 0.3388 Block No 2.0 0.0 2.0 0.0 -397.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 2.0 5.0
32 100037 0 Cash loans F N N 0 90000.000 199008.0 20893.5 180000.0 Unaccompanied Working Secondary / secondary special Civil marriage House / apartment 0.010032 -12974 -4404 -7123.0 -4464 NaN 1 1 1 1 0 0 Laborers 2.0 2 2 SATURDAY 11 0 0 0 0 0 0 Business Entity Type 2 NaN 0.559467 0.798137 0.0928 NaN 0.9801 NaN NaN 0.0000 0.2069 0.1667 NaN NaN NaN 0.0602 NaN 0.0436 0.0945 NaN 0.9801 NaN NaN 0.0000 0.2069 0.1667 NaN NaN NaN 0.0627 NaN 0.0461 0.0937 NaN 0.9801 NaN NaN 0.00 0.2069 0.1667 NaN NaN NaN 0.0613 NaN 0.0445 NaN block of flats 0.0684 Panel No 0.0 0.0 0.0 0.0 -2766.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 2.0
33 100039 0 Cash loans M Y N 1 360000.000 733315.5 39069.0 679500.0 Unaccompanied Commercial associate Secondary / secondary special Married House / apartment 0.015221 -11694 -2060 -3557.0 -3557 3.0 1 1 0 1 0 0 Drivers 3.0 2 2 THURSDAY 10 0 0 0 1 1 0 Self-employed NaN 0.321745 0.411849 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 2.0 0.0 2.0 0.0 -697.0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 1.0 1.0
34 100040 0 Cash loans F N Y 0 135000.000 1125000.0 32895.0 1125000.0 Unaccompanied State servant Higher education Married House / apartment 0.019689 -15997 -4585 -5735.0 -4067 NaN 1 1 0 1 0 0 Core staff 2.0 2 2 FRIDAY 14 0 0 0 0 0 0 Security Ministries NaN 0.172498 NaN 0.0825 0.0804 0.9762 0.6736 0.0056 0.0000 0.1379 0.1667 0.2083 0.0380 0.0672 0.0637 0.0000 0.0000 0.0840 0.0835 0.9762 0.6864 0.0057 0.0000 0.1379 0.1667 0.2083 0.0389 0.0735 0.0664 0.0000 0.0000 0.0833 0.0804 0.9762 0.6780 0.0057 0.00 0.1379 0.1667 0.2083 0.0387 0.0684 0.0648 0.0000 0.0000 reg oper account block of flats 0.0532 Stone, brick No 0.0 0.0 0.0 0.0 -3019.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NaN NaN NaN NaN NaN NaN
35 100041 0 Cash loans F N N 0 112500.000 450000.0 44509.5 450000.0 Unaccompanied Working Higher education Married House / apartment 0.008575 -12158 -1275 -6265.0 -2009 NaN 1 1 1 1 1 0 Sales staff 2.0 2 2 MONDAY 19 0 0 0 0 0 0 Self-employed NaN 0.663158 0.678568 0.0948 0.0792 0.9861 0.8096 NaN 0.0000 0.1724 0.1667 NaN 0.0888 NaN 0.0582 NaN NaN 0.0966 0.0822 0.9861 0.8171 NaN 0.0000 0.1724 0.1667 NaN 0.0908 NaN 0.0606 NaN NaN 0.0958 0.0792 0.9861 0.8121 NaN 0.00 0.1724 0.1667 NaN 0.0903 NaN 0.0592 NaN NaN reg oper account block of flats 0.0749 Stone, brick No 0.0 0.0 0.0 0.0 -1285.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 0.0
36 100043 0 Cash loans F N Y 2 198000.000 641173.5 23157.0 553500.0 Unaccompanied Commercial associate Secondary / secondary special Married House / apartment 0.018850 -17199 -768 -63.0 -735 NaN 1 1 1 1 1 0 Private service staff 4.0 2 2 THURSDAY 13 0 0 0 0 0 0 Other 0.842763 0.681699 0.754406 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 0.0 0.0 0.0 0.0 -2411.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 1.0 1.0
37 100044 0 Cash loans M N Y 0 121500.000 454500.0 15151.5 454500.0 Unaccompanied Working Secondary / secondary special Married House / apartment 0.030755 -21077 -1288 -5474.0 -4270 NaN 1 1 0 1 1 0 Drivers 2.0 2 2 FRIDAY 10 0 0 0 0 1 1 Transport: type 4 0.804586 0.719799 0.722393 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 0.0 0.0 0.0 0.0 -1541.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 1.0 0.0 0.0
38 100045 0 Cash loans F N Y 0 99000.000 247275.0 17338.5 225000.0 Unaccompanied Pensioner Secondary / secondary special Married House / apartment 0.006207 -23920 365243 -9817.0 -4969 NaN 1 0 0 1 1 0 NaN 2.0 2 2 FRIDAY 11 0 0 0 0 0 0 XNA NaN 0.650765 0.751724 NaN NaN 0.9851 NaN NaN 0.0400 0.0345 0.3333 NaN 0.0566 NaN 0.0922 NaN NaN NaN NaN 0.9851 NaN NaN 0.0403 0.0345 0.3333 NaN 0.0579 NaN 0.0960 NaN NaN NaN NaN 0.9851 NaN NaN 0.04 0.0345 0.3333 NaN 0.0576 NaN 0.0938 NaN NaN NaN block of flats 0.0786 Stone, brick No 0.0 0.0 0.0 0.0 0.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 2.0
39 100046 0 Revolving loans M Y Y 0 180000.000 540000.0 27000.0 540000.0 Unaccompanied Working Higher education Married House / apartment 0.025164 -16126 -1761 -8236.0 -4292 3.0 1 1 0 1 0 0 Managers 2.0 2 2 TUESDAY 8 0 0 0 0 0 0 Business Entity Type 3 NaN 0.738053 0.605836 0.0814 0.0994 0.9831 0.7688 0.0142 0.0000 0.2069 0.1667 0.2083 0.1404 0.0656 0.0771 0.0039 0.0227 0.0830 0.1032 0.9831 0.7779 0.0143 0.0000 0.2069 0.1667 0.2083 0.1436 0.0716 0.0804 0.0039 0.0241 0.0822 0.0994 0.9831 0.7719 0.0143 0.00 0.2069 0.1667 0.2083 0.1429 0.0667 0.0785 0.0039 0.0232 org spec account block of flats 0.0734 Stone, brick No 1.0 0.0 1.0 0.0 -540.0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 1.0 0.0 1.0
40 100047 1 Cash loans M N Y 0 202500.000 1193580.0 35028.0 855000.0 Unaccompanied Commercial associate Secondary / secondary special Married House / apartment 0.025164 -17482 -1262 -1182.0 -1029 NaN 1 1 0 1 0 0 Laborers 2.0 2 2 TUESDAY 9 0 0 0 0 0 0 Business Entity Type 3 NaN 0.306841 0.320163 0.1309 0.1250 0.9960 0.9456 0.0822 0.1600 0.1379 0.2500 0.2917 0.0142 0.1059 0.1267 0.0039 0.0078 0.1334 0.1297 0.9960 0.9477 0.0830 0.1611 0.1379 0.2500 0.2917 0.0145 0.1157 0.1320 0.0039 0.0082 0.1322 0.1250 0.9960 0.9463 0.0827 0.16 0.1379 0.2500 0.2917 0.0144 0.1077 0.1290 0.0039 0.0079 org spec account block of flats 0.1463 Stone, brick No 0.0 0.0 0.0 0.0 -1075.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 2.0 0.0 4.0
41 100048 0 Cash loans F N Y 0 202500.000 604152.0 29196.0 540000.0 Unaccompanied Working Secondary / secondary special Married House / apartment 0.009175 -16971 -475 -3148.0 -513 NaN 1 1 0 1 1 0 Laborers 2.0 2 2 MONDAY 12 0 1 1 0 1 1 Industry: type 1 NaN 0.037315 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 4.0 0.0 4.0 0.0 -458.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 5.0
42 100049 1 Cash loans F N N 0 135000.000 288873.0 16258.5 238500.0 Unaccompanied Working Secondary / secondary special Civil marriage House / apartment 0.007305 -13384 -3597 -45.0 -4409 NaN 1 1 1 1 1 0 Sales staff 2.0 3 3 THURSDAY 11 0 0 0 0 0 0 Self-employed 0.468208 0.674203 0.399676 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 1.0 0.0 1.0 0.0 -1480.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 2.0
43 100050 0 Cash loans F N Y 0 108000.000 746280.0 42970.5 675000.0 Unaccompanied Pensioner Higher education Single / not married House / apartment 0.010966 -23548 365243 -5745.0 -4576 NaN 1 0 0 1 0 0 NaN 1.0 2 2 WEDNESDAY 9 0 0 0 0 0 0 XNA NaN 0.766138 0.684828 0.2186 0.1232 0.9851 0.7960 0.0528 0.2400 0.2069 0.3333 0.3750 0.1154 0.1774 0.2113 0.0039 0.0051 0.2227 0.1279 0.9851 0.8040 0.0533 0.2417 0.2069 0.3333 0.3750 0.1181 0.1938 0.2202 0.0039 0.0054 0.2207 0.1232 0.9851 0.7987 0.0531 0.24 0.2069 0.3333 0.3750 0.1175 0.1804 0.2151 0.0039 0.0052 reg oper spec account block of flats 0.1903 Panel No 0.0 0.0 0.0 0.0 -491.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 3.0
44 100051 0 Cash loans M N Y 0 202500.000 661702.5 48280.5 598500.0 Unaccompanied Working Secondary / secondary special Civil marriage House / apartment 0.007114 -9827 -758 -4166.0 -2380 NaN 1 1 1 1 0 0 NaN 2.0 2 2 WEDNESDAY 12 0 0 0 0 1 1 Business Entity Type 3 NaN 0.631519 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 1.0 0.0 1.0 0.0 -556.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NaN NaN NaN NaN NaN NaN
45 100052 0 Revolving loans F N Y 1 90000.000 180000.0 9000.0 180000.0 Family Working Secondary / secondary special Civil marriage House / apartment 0.009334 -7974 -895 -6591.0 -642 NaN 1 1 1 1 0 0 Sales staff 3.0 2 2 MONDAY 10 0 0 0 0 0 0 Self-employed NaN 0.535542 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 1.0 1.0 1.0 1.0 -462.0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NaN NaN NaN NaN NaN NaN
46 100053 0 Cash loans F N Y 0 202500.000 305221.5 17649.0 252000.0 Family Pensioner Secondary / secondary special Single / not married House / apartment 0.019689 -20373 365243 -7760.0 -1993 NaN 1 0 0 1 0 0 NaN 1.0 2 2 MONDAY 8 0 0 0 0 0 0 XNA NaN 0.247665 0.475850 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 0.0 0.0 0.0 0.0 -649.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 2.0 4.0
47 100054 0 Cash loans F N Y 0 99000.000 260640.0 26838.0 225000.0 Unaccompanied Working Secondary / secondary special Married House / apartment 0.022625 -20121 -5332 -9197.0 -3283 NaN 1 1 1 1 1 0 Medicine staff 2.0 2 2 WEDNESDAY 16 0 0 0 0 1 1 Business Entity Type 3 NaN 0.089241 0.728141 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 4.0 0.0 4.0 0.0 -1247.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 2.0
48 100055 0 Cash loans F N N 0 130500.000 1350000.0 37255.5 1350000.0 Unaccompanied State servant Secondary / secondary special Married House / apartment 0.020713 -19727 -4411 -6225.0 -3199 NaN 1 1 1 1 1 0 Managers 2.0 3 3 THURSDAY 10 0 0 0 0 0 0 Government 0.710427 0.589187 0.466864 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 0.0 0.0 0.0 0.0 -784.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 0.0
49 100056 0 Cash loans M Y Y 0 360000.000 1506816.0 49927.5 1350000.0 Unaccompanied Working Secondary / secondary special Married House / apartment 0.010006 -18264 -9523 -5504.0 -1797 3.0 1 1 0 1 0 0 Laborers 2.0 2 2 THURSDAY 10 0 0 0 0 1 1 Transport: type 2 NaN 0.523934 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 1.0 1.0 1.0 1.0 -1498.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NaN NaN NaN NaN NaN NaN
50 100058 0 Revolving loans F N Y 0 54000.000 135000.0 6750.0 135000.0 Unaccompanied State servant Higher education Married House / apartment 0.014520 -12406 -1176 -1149.0 -2576 NaN 1 1 0 0 0 0 Core staff 2.0 2 2 THURSDAY 9 0 0 0 0 0 0 School 0.656225 0.450850 0.479449 0.0664 0.0442 0.9980 NaN NaN 0.0800 0.0552 0.4083 0.4500 0.0085 NaN 0.0651 NaN 0.0160 0.0441 0.0306 0.9980 NaN NaN 0.0806 0.0690 0.3750 0.4167 0.0078 NaN 0.0487 NaN 0.0168 0.0822 0.0408 0.9980 NaN NaN 0.08 0.0690 0.3750 0.4167 0.0087 NaN 0.0747 NaN 0.0163 NaN block of flats 0.0403 Panel No 0.0 0.0 0.0 0.0 0.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0.0 0.0 0.0 0.0 0.0 0.0
51 100059 0 Cash loans M Y Y 1 540000.000 675000.0 34596.0 675000.0 Unaccompanied Commercial associate Higher education Married House / apartment 0.026392 -18632 -6977 -3904.0 -1937 7.0 1 1 0 1 1 0 Managers 3.0 2 2 TUESDAY 10 0 0 0 0 0 0 Services 0.311510 0.713355 0.309275 0.5309 0.2491 0.9811 0.7416 NaN 0.8800 0.3793 0.4583 0.0000 0.3977 NaN 0.5878 NaN 0.0487 0.5410 0.2585 0.9811 0.7517 NaN 0.8862 0.3793 0.4583 0.0000 0.4067 NaN 0.6124 NaN 0.0516 0.5361 0.2491 0.9811 0.7451 NaN 0.88 0.3793 0.4583 0.0000 0.4046 NaN 0.5983 NaN 0.0498 NaN block of flats 0.5149 Panel No 2.0 0.0 2.0 0.0 -1285.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 1.0 0.0 0.0 3.0
52 100060 0 Cash loans M Y N 0 76500.000 454500.0 14661.0 454500.0 Unaccompanied Working Secondary / secondary special Married House / apartment 0.028663 -12282 -778 -1824.0 -1992 13.0 1 1 1 1 0 0 NaN 2.0 2 2 TUESDAY 10 0 0 0 0 0 0 Emergency 0.244562 0.705947 0.799297 0.0227 NaN 0.9806 NaN NaN NaN 0.1034 0.0417 NaN NaN NaN 0.0114 NaN 0.0226 0.0231 NaN 0.9806 NaN NaN NaN 0.1034 0.0417 NaN NaN NaN 0.0119 NaN 0.0239 0.0229 NaN 0.9806 NaN NaN NaN 0.1034 0.0417 NaN NaN NaN 0.0116 NaN 0.0230 NaN block of flats 0.0139 Stone, brick No 4.0 0.0 4.0 0.0 -466.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 1.0 0.0 0.0
53 100061 0 Cash loans F N Y 2 225000.000 314055.0 16164.0 238500.0 Unaccompanied Commercial associate Higher education Married House / apartment 0.006629 -10473 -1392 -2025.0 -1240 NaN 1 1 0 1 0 0 Core staff 4.0 2 2 THURSDAY 3 0 0 0 1 1 0 Self-employed 0.189596 0.702979 0.701696 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 0.0 0.0 0.0 0.0 -570.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 5.0
54 100062 0 Cash loans M Y N 0 81000.000 675000.0 32472.0 675000.0 Unaccompanied Pensioner Secondary / secondary special Married House / apartment 0.010006 -18611 365243 -6936.0 -2142 8.0 1 0 0 1 1 0 NaN 2.0 2 2 TUESDAY 17 0 0 0 0 0 0 XNA NaN 0.662865 0.738020 0.2165 0.1678 0.9846 0.7892 NaN 0.2400 0.2069 0.3333 0.3750 0.1071 NaN NaN NaN 0.0151 0.2206 0.1741 0.9846 0.7975 NaN 0.2417 0.2069 0.3333 0.3750 0.1095 NaN NaN NaN 0.0160 0.2186 0.1678 0.9846 0.7920 NaN 0.24 0.2069 0.3333 0.3750 0.1089 NaN NaN NaN 0.0154 reg oper account block of flats 0.2048 Panel No 0.0 0.0 0.0 0.0 -462.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 2.0
55 100063 0 Cash loans F N Y 0 180000.000 837427.5 45558.0 666000.0 Unaccompanied Working Higher education Married House / apartment 0.022625 -12955 -4106 -7054.0 -5164 NaN 1 1 0 1 1 1 Managers 2.0 2 2 MONDAY 15 0 0 0 0 0 0 Medicine 0.552772 0.616637 0.070109 0.1186 0.0660 0.9801 0.7280 0.0385 0.0400 0.0345 0.3333 0.0417 0.0606 0.0958 0.0822 0.0039 0.0002 0.1208 0.0685 0.9801 0.7387 0.0388 0.0403 0.0345 0.3333 0.0417 0.0620 0.1047 0.0856 0.0039 0.0002 0.1197 0.0660 0.9801 0.7316 0.0387 0.04 0.0345 0.3333 0.0417 0.0616 0.0975 0.0837 0.0039 0.0002 reg oper spec account block of flats 0.0857 Stone, brick No 1.0 1.0 1.0 1.0 -1547.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 1.0 0.0 4.0
56 100064 0 Cash loans F N N 0 67500.000 298728.0 15381.0 202500.0 Family Pensioner Secondary / secondary special Single / not married Municipal apartment 0.019101 -21621 365243 -2019.0 -1344 NaN 1 0 0 1 0 0 NaN 1.0 2 2 MONDAY 9 0 0 0 0 0 0 XNA NaN 0.622708 0.169429 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 0.0 0.0 0.0 0.0 0.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 0.0
57 100068 0 Revolving loans M N Y 0 81000.000 247500.0 12375.0 247500.0 Unaccompanied Working Secondary / secondary special Married House / apartment 0.031329 -18248 -2351 -4375.0 -1721 NaN 1 1 1 1 1 0 Security staff 2.0 2 2 WEDNESDAY 14 0 0 0 0 1 1 Security 0.237720 0.609135 0.513694 0.1227 0.1572 0.9886 0.8436 0.0029 0.0000 0.3448 0.1667 0.2083 0.0554 0.0992 0.1177 0.0039 0.0052 0.1250 0.1631 0.9886 0.8497 0.0029 0.0000 0.3448 0.1667 0.2083 0.0566 0.1084 0.1226 0.0039 0.0055 0.1239 0.1572 0.9886 0.8457 0.0029 0.00 0.3448 0.1667 0.2083 0.0563 0.1009 0.1198 0.0039 0.0053 org spec account block of flats 0.0926 Panel No 2.0 0.0 2.0 0.0 -167.0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 0.0
58 100069 0 Cash loans M Y Y 1 360000.000 640458.0 27265.5 517500.0 Unaccompanied Working Secondary / secondary special Separated House / apartment 0.007330 -14186 -1743 -2128.0 -4982 10.0 1 1 0 1 0 0 Laborers 2.0 2 2 MONDAY 11 0 1 1 0 1 1 Transport: type 4 NaN 0.256737 0.707699 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 0.0 0.0 0.0 0.0 -112.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 1.0 3.0
59 100070 0 Cash loans M Y Y 0 540000.000 1227901.5 46899.0 1129500.0 Unaccompanied Working Higher education Widow House / apartment 0.032561 -20544 -2091 -6846.0 -566 2.0 1 1 1 1 0 0 Managers 1.0 1 1 TUESDAY 14 0 0 0 0 0 0 Business Entity Type 3 NaN 0.653597 0.339288 0.0256 0.0000 0.9354 0.1160 0.0076 0.0000 0.1172 0.1000 0.1417 0.0664 0.0202 0.0305 0.0039 0.0084 0.0063 0.0000 0.9136 0.0000 0.0006 0.0000 0.0690 0.0833 0.1250 0.0819 0.0055 0.0072 0.0000 0.0000 0.0104 0.0000 0.9136 0.0000 0.0015 0.00 0.0690 0.0833 0.1250 0.0815 0.0086 0.0070 0.0039 0.0013 reg oper account block of flats 0.0058 Stone, brick No 0.0 0.0 0.0 0.0 -1377.0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 0.0
60 100071 0 Cash loans F N Y 0 180000.000 1663987.5 86989.5 1588500.0 Family Commercial associate Secondary / secondary special Married House / apartment 0.018801 -20121 -2446 -6755.0 -2177 NaN 1 1 0 1 0 0 Accountants 2.0 2 2 MONDAY 12 0 0 0 0 0 0 Industry: type 11 NaN 0.570666 0.697147 0.0876 0.0496 0.9841 0.7824 0.0096 0.0400 0.0345 0.4583 0.5000 0.0208 0.0698 0.0767 0.0077 0.0210 0.0893 0.0515 0.9841 0.7909 0.0097 0.0403 0.0345 0.4583 0.5000 0.0213 0.0762 0.0799 0.0078 0.0222 0.0885 0.0496 0.9841 0.7853 0.0097 0.04 0.0345 0.4583 0.5000 0.0212 0.0710 0.0781 0.0078 0.0214 reg oper spec account block of flats 0.0701 Stone, brick No 0.0 0.0 0.0 0.0 -1572.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 2.0 0.0 1.0
61 100072 0 Cash loans M N N 0 180000.000 1080000.0 44118.0 1080000.0 Unaccompanied Working Incomplete higher Single / not married With parents 0.010006 -7907 -1324 -4557.0 -586 NaN 1 1 0 1 0 1 Sales staff 1.0 2 1 TUESDAY 9 0 0 0 0 0 0 Trade: type 2 NaN 0.026541 0.434733 0.0928 0.0955 0.9856 0.8028 0.0130 0.0000 0.2069 0.1667 0.2083 0.1147 0.0756 0.0913 0.0000 0.0000 0.0945 0.0991 0.9856 0.8105 0.0131 0.0000 0.2069 0.1667 0.2083 0.1173 0.0826 0.0951 0.0000 0.0000 0.0937 0.0955 0.9856 0.8054 0.0131 0.00 0.2069 0.1667 0.2083 0.1167 0.0770 0.0929 0.0000 0.0000 reg oper account block of flats 0.0804 Panel No 0.0 0.0 0.0 0.0 -725.0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 4.0
62 100073 0 Cash loans M Y Y 0 324000.000 1130760.0 40189.5 810000.0 Unaccompanied Pensioner Higher education Civil marriage House / apartment 0.018850 -22843 365243 -1042.0 -3967 10.0 1 0 0 1 0 0 NaN 2.0 2 2 THURSDAY 9 0 0 0 0 0 0 XNA NaN 0.733051 0.234015 0.1928 0.2309 0.9955 NaN NaN 0.1600 0.1379 0.6667 NaN 0.1427 NaN 0.2818 NaN 0.1341 0.1964 0.2396 0.9955 NaN NaN 0.1611 0.1379 0.6667 NaN 0.1459 NaN 0.2936 NaN 0.1420 0.1947 0.2309 0.9955 NaN NaN 0.16 0.1379 0.6667 NaN 0.1452 NaN 0.2869 NaN 0.1369 NaN block of flats 0.2522 Stone, brick No 0.0 0.0 0.0 0.0 -2385.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 1.0 1.0
63 100075 0 Cash loans F N N 0 112500.000 95940.0 10462.5 90000.0 Family Working Higher education Single / not married House / apartment 0.004960 -12240 -3720 -5246.0 -4541 NaN 1 1 0 1 0 0 NaN 1.0 2 2 MONDAY 14 0 0 0 0 0 0 University 0.299606 0.664544 0.492060 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 0.0 0.0 0.0 0.0 -280.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 5.0
64 100076 0 Cash loans M Y N 0 180000.000 315000.0 9679.5 315000.0 NaN Commercial associate Secondary / secondary special Married House / apartment 0.028663 -13866 -2546 -5448.0 -4097 1.0 1 1 0 1 0 0 Drivers 2.0 2 2 MONDAY 12 0 0 0 0 0 0 Transport: type 3 NaN 0.584887 0.095070 0.0124 NaN 0.9677 0.5580 0.0013 0.0000 0.0690 0.0417 0.0417 0.0489 0.0101 0.0100 0.0000 0.0000 0.0126 NaN 0.9677 0.5753 0.0013 0.0000 0.0690 0.0417 0.0417 0.0500 0.0110 0.0105 0.0000 0.0000 0.0125 NaN 0.9677 0.5639 0.0013 0.00 0.0690 0.0417 0.0417 0.0498 0.0103 0.0102 0.0000 0.0000 reg oper account block of flats 0.0079 Block No 0.0 0.0 0.0 0.0 -3.0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 0.0
65 100077 0 Cash loans M N Y 0 180000.000 1256400.0 40657.5 900000.0 Unaccompanied Commercial associate Secondary / secondary special Single / not married House / apartment 0.072508 -12049 -579 -6105.0 -1 NaN 1 1 0 1 0 0 Core staff 1.0 1 1 WEDNESDAY 12 0 0 0 0 0 0 Police 0.286783 0.647348 0.562060 0.1433 0.0993 0.9811 0.7416 0.0000 0.1600 0.1379 0.3333 0.3750 0.0000 0.1168 0.1367 0.0000 0.0207 0.1460 0.1030 0.9811 0.7517 0.0000 0.1611 0.1379 0.3333 0.3750 0.0000 0.1276 0.1425 0.0000 0.0219 0.1447 0.0993 0.9811 0.7451 0.0000 0.16 0.1379 0.3333 0.3750 0.0000 0.1189 0.1392 0.0000 0.0211 reg oper account block of flats 0.1114 Panel No 4.0 0.0 4.0 0.0 -295.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 8.0
66 100078 0 Cash loans F Y Y 0 180000.000 1035000.0 43983.0 1035000.0 Unaccompanied State servant Higher education Married House / apartment 0.028663 -19550 -4856 -6836.0 -3084 21.0 1 1 0 1 0 0 Core staff 2.0 2 2 TUESDAY 9 0 0 0 0 0 0 Government 0.796161 0.517270 0.429424 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 0.0 0.0 0.0 0.0 -1594.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 3.0
67 100079 0 Revolving loans M N Y 0 225000.000 270000.0 13500.0 270000.0 Unaccompanied Working Secondary / secondary special Separated Municipal apartment 0.007330 -15359 -114 -5076.0 -2922 NaN 1 1 0 1 0 0 Laborers 1.0 2 2 THURSDAY 9 0 0 0 0 1 1 Construction 0.202477 0.561923 0.413597 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 0.0 0.0 0.0 0.0 -662.0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 1.0 0.0 0.0
68 100080 0 Revolving loans F N N 1 157500.000 450000.0 22500.0 450000.0 Unaccompanied Commercial associate Secondary / secondary special Civil marriage House / apartment 0.010966 -11956 -3154 -8118.0 -4143 NaN 1 1 0 1 0 0 Sales staff 3.0 2 2 SUNDAY 10 0 0 0 0 0 0 Kindergarten 0.411506 0.123888 0.832785 0.2175 0.1455 0.9891 0.8504 0.0443 0.2400 0.2069 0.3333 0.3750 0.1343 0.1774 0.2129 0.0000 0.0039 0.2216 0.1509 0.9891 0.8563 0.0447 0.2417 0.2069 0.3333 0.3750 0.1373 0.1938 0.2218 0.0000 0.0041 0.2196 0.1455 0.9891 0.8524 0.0445 0.24 0.2069 0.3333 0.3750 0.1366 0.1804 0.2167 0.0000 0.0040 reg oper spec account block of flats 0.1925 Panel No NaN NaN NaN NaN -1711.0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 5.0
69 100081 0 Cash loans M Y N 0 180000.000 450000.0 21109.5 450000.0 Unaccompanied Commercial associate Secondary / secondary special Civil marriage House / apartment 0.014520 -10848 -121 -4809.0 -3494 20.0 1 1 0 1 0 0 Laborers 2.0 2 2 SATURDAY 9 0 0 0 0 0 0 Other NaN 0.645438 NaN 0.0670 0.0000 0.9752 0.6600 0.0068 0.0000 0.1379 0.1667 0.2083 0.0483 0.0538 0.0506 0.0039 0.0465 0.0683 0.0000 0.9752 0.6733 0.0069 0.0000 0.1379 0.1667 0.2083 0.0494 0.0588 0.0528 0.0039 0.0492 0.0677 0.0000 0.9752 0.6645 0.0069 0.00 0.1379 0.1667 0.2083 0.0491 0.0547 0.0516 0.0039 0.0475 reg oper account block of flats 0.0436 Stone, brick No 1.0 0.0 1.0 0.0 -2519.0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 NaN NaN NaN NaN NaN NaN
70 100082 0 Cash loans M N N 2 180000.000 450000.0 21109.5 450000.0 Unaccompanied Commercial associate Higher education Married House / apartment 0.018850 -12009 -2844 -1377.0 -3656 NaN 1 1 1 1 1 0 Laborers 4.0 2 2 MONDAY 12 0 0 0 0 0 0 Business Entity Type 3 0.357707 0.686505 0.394495 0.0722 0.0487 0.9826 0.7620 0.0085 0.0000 0.1379 0.1667 0.2083 0.0176 0.0588 0.0505 0.0000 0.0000 0.0735 0.0505 0.9826 0.7713 0.0086 0.0000 0.1379 0.1667 0.2083 0.0180 0.0643 0.0526 0.0000 0.0000 0.0729 0.0487 0.9826 0.7652 0.0086 0.00 0.1379 0.1667 0.2083 0.0179 0.0599 0.0514 0.0000 0.0000 reg oper spec account block of flats 0.0444 Panel No 0.0 0.0 0.0 0.0 -1003.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 1.0 0.0 2.0
71 100083 0 Cash loans M Y Y 0 103500.000 573628.5 24435.0 463500.0 Unaccompanied Working Secondary / secondary special Married House / apartment 0.009657 -15406 -892 -341.0 -4176 22.0 1 1 0 1 0 0 Laborers 2.0 2 2 MONDAY 18 0 0 0 0 0 0 Business Entity Type 3 0.270766 0.707126 0.597192 0.1464 0.1291 0.9861 0.8096 0.1080 0.0400 0.0345 0.3333 0.3750 0.0000 0.1194 0.0933 0.0000 0.0000 0.1492 0.1340 0.9861 0.8171 0.1090 0.0403 0.0345 0.3333 0.3750 0.0000 0.1304 0.0972 0.0000 0.0000 0.1478 0.1291 0.9861 0.8121 0.1087 0.04 0.0345 0.3333 0.3750 0.0000 0.1214 0.0949 0.0000 0.0000 reg oper account block of flats 0.1324 Stone, brick No 2.0 1.0 2.0 1.0 -2053.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 3.0
72 100084 0 Cash loans M N Y 2 225000.000 495351.0 26140.5 459000.0 Unaccompanied Working Secondary / secondary special Separated House / apartment 0.024610 -14860 -1156 -23.0 -1072 NaN 1 1 0 1 0 0 Laborers 3.0 2 2 FRIDAY 10 0 0 0 0 0 0 Business Entity Type 3 NaN 0.570527 0.694093 0.0165 0.0000 0.9727 0.6260 NaN 0.0000 0.0690 0.0417 NaN 0.0249 NaN 0.0110 NaN 0.0000 0.0168 0.0000 0.9727 0.6406 NaN 0.0000 0.0690 0.0417 NaN 0.0255 NaN 0.0114 NaN 0.0000 0.0167 0.0000 0.9727 0.6310 NaN 0.00 0.0690 0.0417 NaN 0.0254 NaN 0.0112 NaN 0.0000 NaN block of flats 0.0093 Block No 1.0 0.0 1.0 0.0 -1475.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0.0 0.0 0.0 0.0 1.0 0.0
73 100085 0 Cash loans M N Y 1 157500.000 755190.0 28894.5 675000.0 Unaccompanied Working Secondary / secondary special Married House / apartment 0.016612 -11187 -2224 -3253.0 -3723 NaN 1 1 1 1 0 0 Drivers 3.0 2 2 WEDNESDAY 12 0 0 0 0 0 0 Business Entity Type 1 NaN 0.368719 0.358951 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 7.0 0.0 7.0 0.0 -1175.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 0.0
74 100086 0 Cash loans F N Y 1 135000.000 675000.0 21775.5 675000.0 Unaccompanied Commercial associate Higher education Widow House / apartment 0.019101 -14264 -2492 -4327.0 -4517 NaN 1 1 0 1 0 0 Laborers 2.0 2 2 THURSDAY 12 0 0 0 0 0 0 Self-employed NaN 0.569873 0.198620 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 0.0 0.0 0.0 0.0 -1597.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 1.0 4.0 2.0
75 100087 0 Cash loans F N Y 1 202500.000 1288350.0 37800.0 1125000.0 Unaccompanied Working Secondary / secondary special Married House / apartment 0.003069 -17542 -2011 -1529.0 -988 NaN 1 1 0 1 0 0 Cooking staff 3.0 3 3 FRIDAY 11 0 0 0 0 0 0 Self-employed NaN 0.572064 0.191822 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 2.0 1.0 2.0 1.0 -1230.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 3.0
76 100088 0 Revolving loans F N N 0 112500.000 135000.0 6750.0 135000.0 Unaccompanied Commercial associate Higher education Married House / apartment 0.022800 -19467 -4066 -4623.0 -2975 NaN 1 1 0 1 0 0 NaN 2.0 2 2 THURSDAY 15 0 0 0 0 0 0 School NaN 0.585174 0.408359 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 0.0 0.0 0.0 0.0 -1537.0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 2.0
77 100089 0 Cash loans M N Y 0 135000.000 337761.0 15781.5 256500.0 Unaccompanied Working Secondary / secondary special Civil marriage House / apartment 0.025164 -9666 -2811 -4437.0 -2342 NaN 1 1 0 1 1 0 Laborers 2.0 2 2 TUESDAY 12 0 0 0 0 0 0 Self-employed NaN 0.550058 0.535276 0.0722 0.0752 0.6508 0.6736 0.0080 0.0000 0.1379 0.1667 0.2083 0.0671 0.0664 0.0639 0.0039 0.0029 0.0630 0.0653 0.0000 0.6864 0.0081 0.0000 0.1379 0.1667 0.2083 0.0581 0.0725 0.0555 0.0039 0.0031 0.0729 0.0800 0.9762 0.6780 0.0081 0.00 0.1379 0.1667 0.2083 0.0659 0.0676 0.0701 0.0039 0.0030 reg oper account block of flats 0.0472 Panel No 2.0 0.0 2.0 0.0 -92.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 3.0
78 100093 0 Cash loans F N Y 1 94500.000 585000.0 28273.5 585000.0 Unaccompanied Working Secondary / secondary special Married House / apartment 0.028663 -18253 -721 -1701.0 -1779 NaN 1 1 0 1 1 0 Core staff 3.0 2 2 TUESDAY 16 0 0 0 0 0 0 Postal 0.592701 0.488600 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 0.0 0.0 0.0 0.0 0.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 3.0
79 100094 0 Cash loans F N Y 0 108000.000 113760.0 5301.0 90000.0 Unaccompanied Pensioner Higher education Single / not married House / apartment 0.030755 -22935 365243 -12623.0 -3442 NaN 1 0 0 1 0 0 NaN 1.0 2 2 THURSDAY 10 0 0 0 0 0 0 XNA NaN 0.582735 0.452534 0.1474 0.0669 0.9578 NaN NaN 0.0000 0.3103 0.2083 NaN 0.0663 NaN 0.1648 NaN 0.0427 0.1502 0.0694 0.9578 NaN NaN 0.0000 0.3103 0.2083 NaN 0.0679 NaN 0.1717 NaN 0.0452 0.1489 0.0669 0.9578 NaN NaN 0.00 0.3103 0.2083 NaN 0.0675 NaN 0.1678 NaN 0.0436 NaN block of flats 0.1584 Stone, brick No 3.0 0.0 3.0 0.0 -260.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 0.0
80 100095 0 Revolving loans F N Y 2 67500.000 135000.0 6750.0 135000.0 Unaccompanied Working Secondary / secondary special Married House / apartment 0.020246 -18693 -1434 -10255.0 -2241 NaN 1 1 0 1 1 0 Sales staff 4.0 3 3 THURSDAY 8 0 0 0 0 0 0 Trade: type 7 0.586698 0.304506 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 0.0 0.0 0.0 0.0 -1040.0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NaN NaN NaN NaN NaN NaN
81 100096 1 Cash loans F N Y 0 81000.000 252000.0 14593.5 252000.0 Unaccompanied Pensioner Secondary / secondary special Married House / apartment 0.028663 -24794 365243 -5391.0 -4199 NaN 1 0 0 1 0 0 NaN 2.0 2 2 THURSDAY 10 0 0 0 0 0 0 XNA NaN 0.023952 0.720944 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 1.0 1.0 1.0 1.0 0.0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 0.0
82 100097 0 Cash loans M Y Y 0 157500.000 760225.5 30280.5 679500.0 Unaccompanied Working Secondary / secondary special Married House / apartment 0.035792 -17527 -2442 -3500.0 -983 20.0 1 1 0 1 0 0 NaN 2.0 2 2 THURSDAY 10 0 0 0 0 1 1 Business Entity Type 3 NaN 0.357342 0.644679 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 6.0 0.0 6.0 0.0 -371.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 1.0 0.0 0.0 0.0
83 100098 0 Revolving loans M Y N 0 225000.000 270000.0 13500.0 270000.0 Unaccompanied Working Secondary / secondary special Civil marriage House / apartment 0.018850 -17390 -924 -7211.0 -907 1.0 1 1 0 1 0 0 Drivers 2.0 2 2 WEDNESDAY 9 0 0 0 0 0 0 Transport: type 3 NaN 0.597081 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 0.0 0.0 0.0 0.0 -617.0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NaN NaN NaN NaN NaN NaN
84 100099 0 Cash loans F N Y 0 360000.000 733315.5 41076.0 679500.0 Unaccompanied Pensioner Secondary / secondary special Separated House / apartment 0.009175 -23032 365243 -9506.0 -793 NaN 1 0 0 1 0 0 NaN 1.0 2 2 THURSDAY 12 1 0 0 0 0 0 XNA NaN 0.660579 0.342529 0.0454 0.0108 0.9742 NaN NaN 0.0000 0.1034 0.1250 NaN 0.0292 NaN 0.0360 NaN 0.0148 0.0462 0.0112 0.9742 NaN NaN 0.0000 0.1034 0.1250 NaN 0.0299 NaN 0.0375 NaN 0.0157 0.0458 0.0108 0.9742 NaN NaN 0.00 0.1034 0.1250 NaN 0.0297 NaN 0.0367 NaN 0.0152 NaN block of flats 0.0315 Stone, brick No 4.0 0.0 4.0 0.0 0.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 3.0
85 100100 0 Cash loans M Y Y 2 202500.000 796396.5 38443.5 643500.0 Unaccompanied Working Higher education Married House / apartment 0.008019 -15909 -1094 -3134.0 -4180 65.0 1 1 0 1 0 1 Managers 4.0 2 2 FRIDAY 17 0 0 0 0 1 1 Industry: type 11 0.447675 0.495765 0.824595 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 0.0 0.0 0.0 0.0 -1876.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 2.0
86 100101 0 Cash loans F Y Y 0 202500.000 343377.0 22072.5 283500.0 Unaccompanied State servant Higher education Single / not married House / apartment 0.072508 -18138 -969 -7421.0 -1681 5.0 1 1 0 1 0 0 High skill tech staff 1.0 1 1 WEDNESDAY 13 0 0 0 0 0 0 Business Entity Type 1 NaN 0.716212 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 0.0 0.0 0.0 0.0 -1585.0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 NaN NaN NaN NaN NaN NaN
87 100102 0 Cash loans F N N 1 126000.000 327024.0 10264.5 270000.0 Unaccompanied Working Secondary / secondary special Single / not married House / apartment 0.024610 -14321 -192 -6913.0 -5942 NaN 1 1 0 1 1 0 Laborers 2.0 2 2 SUNDAY 13 0 0 0 0 0 0 Transport: type 4 0.414794 0.661108 0.477649 0.0041 NaN 0.9722 0.6192 NaN NaN 0.0345 0.0000 0.0417 0.0144 0.0034 0.0048 0.0000 0.0000 0.0042 NaN 0.9722 0.6341 NaN NaN 0.0345 0.0000 0.0417 0.0147 0.0037 0.0050 0.0000 0.0000 0.0042 NaN 0.9722 0.6243 NaN NaN 0.0345 0.0000 0.0417 0.0146 0.0034 0.0049 0.0000 0.0000 reg oper account block of flats 0.0083 Others No 1.0 0.0 1.0 0.0 -2.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0.0 0.0 0.0 6.0 0.0 0.0
88 100103 0 Cash loans F N Y 0 72000.000 450000.0 16965.0 450000.0 Unaccompanied Working Secondary / secondary special Married House / apartment 0.009175 -21112 -1694 -6057.0 -4013 NaN 1 1 1 1 0 0 NaN 2.0 2 2 FRIDAY 12 0 0 0 0 0 0 School NaN 0.718058 0.511892 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 4.0 0.0 4.0 0.0 0.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 0.0
89 100104 0 Cash loans F N Y 0 90000.000 547344.0 30690.0 472500.0 Unaccompanied Working Secondary / secondary special Married House / apartment 0.022625 -18556 -862 -6769.0 -2045 NaN 1 1 0 1 1 0 Waiters/barmen staff 2.0 2 2 WEDNESDAY 17 0 0 0 0 0 0 Business Entity Type 3 0.763137 0.644627 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 1.0 0.0 1.0 0.0 -2591.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 1.0
90 100105 0 Cash loans F N Y 0 193500.000 225000.0 23755.5 225000.0 Unaccompanied Working Secondary / secondary special Married House / apartment 0.031329 -19810 -8862 -4493.0 -3043 NaN 1 1 1 1 1 0 Managers 2.0 2 2 TUESDAY 11 0 0 0 0 0 0 Government 0.765154 0.254760 0.411849 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 0.0 0.0 0.0 0.0 -1733.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 2.0 4.0
91 100108 0 Cash loans F N Y 3 171000.000 545040.0 31288.5 450000.0 Unaccompanied Working Secondary / secondary special Married House / apartment 0.006629 -12780 -1415 -5146.0 -870 NaN 1 1 1 1 1 0 Sales staff 5.0 2 2 WEDNESDAY 11 0 0 0 0 1 1 Business Entity Type 3 NaN 0.756668 0.641368 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 3.0 0.0 3.0 0.0 -546.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 0.0
92 100110 0 Cash loans M Y Y 3 135000.000 373140.0 25065.0 337500.0 Unaccompanied Commercial associate Secondary / secondary special Married House / apartment 0.035792 -10744 -741 -442.0 -2381 21.0 1 1 0 1 0 0 Laborers 5.0 2 2 WEDNESDAY 9 0 0 0 0 1 1 Housing NaN 0.640549 0.328063 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 0.0 0.0 0.0 0.0 0.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 1.0 2.0
93 100111 0 Cash loans F Y N 1 112500.000 862560.0 27954.0 720000.0 Unaccompanied Commercial associate Secondary / secondary special Married With parents 0.022800 -10485 -1249 -4628.0 -3110 17.0 1 1 0 1 0 0 Sales staff 3.0 2 2 MONDAY 11 0 0 0 0 0 0 Business Entity Type 3 0.563323 0.608604 0.228883 0.2619 0.1186 0.9876 0.8300 NaN 0.3200 0.2759 0.3333 0.3750 0.0856 NaN 0.2675 NaN 0.1749 0.2668 0.1231 0.9876 0.8367 NaN 0.3222 0.2759 0.3333 0.3750 0.0876 NaN 0.2787 NaN 0.1852 0.2644 0.1186 0.9876 0.8323 NaN 0.32 0.2759 0.3333 0.3750 0.0871 NaN 0.2723 NaN 0.1786 reg oper account block of flats 0.2576 Stone, brick No 1.0 0.0 1.0 0.0 -1234.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 0.0
94 100112 1 Cash loans M Y Y 0 315000.000 953460.0 64107.0 900000.0 Family Commercial associate Incomplete higher Single / not married With parents 0.030755 -10199 -2015 -4802.0 -1038 2.0 1 1 0 1 0 0 NaN 1.0 2 2 SUNDAY 13 0 0 0 0 1 1 Industry: type 4 NaN 0.432340 0.077499 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 0.0 0.0 0.0 0.0 -591.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 2.0
95 100113 0 Cash loans M Y N 0 76500.000 135000.0 6696.0 135000.0 Unaccompanied Working Secondary / secondary special Single / not married House / apartment 0.028663 -15137 -7980 -9176.0 -4576 11.0 1 1 1 1 0 0 Low-skill Laborers 1.0 2 2 MONDAY 10 0 0 0 0 0 0 Agriculture 0.475762 0.593019 0.789880 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 0.0 0.0 0.0 0.0 -1981.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 2.0
96 100114 0 Cash loans F N Y 0 135000.000 284400.0 22599.0 225000.0 Unaccompanied Commercial associate Higher education Single / not married House / apartment 0.030755 -10734 -693 -7913.0 -1362 NaN 1 1 0 1 0 0 Laborers 1.0 2 2 MONDAY 14 0 0 0 1 1 0 Restaurant 0.395724 0.154937 0.265049 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 0.0 0.0 0.0 0.0 -227.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 2.0
97 100115 0 Cash loans F N N 0 90000.000 315000.0 14683.5 315000.0 Spouse, partner Working Secondary / secondary special Married House / apartment 0.028663 -15371 -367 -3071.0 -4211 NaN 1 1 1 1 1 0 NaN 2.0 2 2 SUNDAY 11 0 0 0 0 1 1 Business Entity Type 3 NaN 0.334300 NaN 0.0247 0.0000 0.9707 NaN NaN 0.0000 0.1034 0.0417 NaN 0.0671 NaN 0.0243 NaN 0.0000 0.0252 0.0000 0.9707 NaN NaN 0.0000 0.1034 0.0417 NaN 0.0686 NaN 0.0253 NaN 0.0000 0.0250 0.0000 0.9707 NaN NaN 0.00 0.1034 0.0417 NaN 0.0682 NaN 0.0248 NaN 0.0000 NaN block of flats 0.0210 Block No 0.0 0.0 0.0 0.0 -40.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 2.0 2.0
98 100116 0 Cash loans F N Y 0 243000.000 900000.0 32017.5 900000.0 Unaccompanied Working Higher education Married House / apartment 0.010006 -14751 -6737 -3390.0 -4788 NaN 1 1 0 1 0 1 Core staff 2.0 2 1 THURSDAY 11 0 0 0 0 1 1 Kindergarten 0.671272 0.700274 0.071055 0.1474 0.4212 0.9975 0.9660 NaN 0.1200 0.1034 0.3750 0.3750 0.0135 0.1059 0.1860 0.0656 0.1969 0.1502 0.4371 0.9975 0.9673 NaN 0.1208 0.1034 0.3750 0.3750 0.0138 0.1157 0.1937 0.0661 0.2084 0.1489 0.4212 0.9975 0.9665 NaN 0.12 0.1034 0.3750 0.3750 0.0138 0.1077 0.1893 0.0660 0.2010 reg oper spec account block of flats 0.2170 Stone, brick No 2.0 0.0 2.0 0.0 -1083.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 0.0
99 100118 0 Cash loans F N Y 0 90000.000 675000.0 26541.0 675000.0 Unaccompanied Pensioner Secondary / secondary special Widow House / apartment 0.026392 -22471 365243 -217.0 -4323 NaN 1 0 0 1 0 0 NaN 1.0 2 2 TUESDAY 15 0 0 0 0 0 0 XNA NaN 0.626303 NaN 0.1361 0.0761 0.9960 0.9456 0.0525 0.1400 0.1207 0.3750 0.0417 0.0814 0.1097 0.1539 0.0058 0.0168 0.0777 0.0400 0.9960 0.9477 0.0295 0.0806 0.0690 0.3750 0.0417 0.0501 0.0652 0.0788 0.0000 0.0000 0.1374 0.0761 0.9960 0.9463 0.0529 0.14 0.1207 0.3750 0.0417 0.0828 0.1116 0.1567 0.0058 0.0172 reg oper account block of flats 0.2241 Panel No 3.0 2.0 3.0 1.0 0.0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NaN NaN NaN NaN NaN NaN
100 100119 0 Revolving loans F N Y 0 54000.000 180000.0 9000.0 180000.0 Unaccompanied Working Secondary / secondary special Married House / apartment 0.015221 -11142 -2419 -4987.0 -467 NaN 1 1 0 1 0 0 Medicine staff 2.0 2 2 SATURDAY 16 0 0 0 0 0 0 Medicine NaN 0.500264 0.077499 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 0.0 0.0 0.0 0.0 -149.0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 0.0
101 100120 0 Cash loans F N N 0 135000.000 781920.0 28215.0 675000.0 Unaccompanied Working Higher education Married House / apartment 0.019689 -9650 -2953 -4329.0 -367 NaN 1 1 1 1 0 0 Core staff 2.0 2 2 FRIDAY 15 0 0 0 0 1 1 Government 0.254352 0.169953 0.121408 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 1.0 0.0 1.0 0.0 -17.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 0.0
102 100121 0 Cash loans M N Y 0 126000.000 787131.0 26145.0 679500.0 Unaccompanied Working Secondary / secondary special Married With parents 0.018209 -16798 -669 -9388.0 -347 NaN 1 1 1 1 1 0 Low-skill Laborers 2.0 3 3 FRIDAY 7 0 0 0 0 0 0 Business Entity Type 2 NaN 0.586150 0.581484 NaN 0.1117 0.9781 NaN NaN NaN 0.2069 0.1667 NaN NaN NaN 0.0987 NaN NaN NaN 0.1159 0.9782 NaN NaN NaN 0.2069 0.1667 NaN NaN NaN 0.1028 NaN NaN NaN 0.1117 0.9781 NaN NaN NaN 0.2069 0.1667 NaN NaN NaN 0.1004 NaN NaN NaN NaN 0.0776 Panel No 5.0 2.0 5.0 2.0 -319.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1.0 1.0 0.0 0.0 0.0 1.0
103 100122 0 Cash loans F N N 1 76500.000 808650.0 26217.0 675000.0 Unaccompanied Commercial associate Higher education Married With parents 0.022800 -10954 -2469 -550.0 -596 NaN 1 1 0 1 1 0 Core staff 3.0 2 2 TUESDAY 11 0 0 0 0 0 0 Other 0.405564 0.047291 0.406617 0.3887 0.2438 0.9990 0.9864 0.1221 0.4264 0.1838 0.5833 0.6250 0.1498 0.2564 0.3606 0.2780 0.0267 0.2174 0.2108 0.9990 0.9869 0.0678 0.2417 0.1034 0.5833 0.6250 0.0965 0.1534 0.2250 0.1556 0.0133 0.3872 0.2032 0.9990 0.9866 0.1201 0.40 0.1724 0.5833 0.6250 0.1040 0.2608 0.3475 0.2601 0.0234 reg oper account block of flats 0.2094 Panel No 5.0 1.0 5.0 1.0 -663.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 0.0
104 100123 0 Cash loans F N N 0 103500.000 675000.0 19737.0 675000.0 Unaccompanied Working Secondary / secondary special Widow House / apartment 0.020246 -23719 -8466 -3917.0 -4036 NaN 1 1 1 1 0 0 Security staff 1.0 3 3 SATURDAY 16 0 0 0 0 0 0 Medicine NaN 0.399366 0.673830 0.0567 0.0629 0.9732 0.6328 0.0484 0.0000 0.1034 0.1667 0.2083 0.0314 0.0420 0.0308 0.0193 0.0148 0.0578 0.0652 0.9732 0.6472 0.0488 0.0000 0.1034 0.1667 0.2083 0.0322 0.0459 0.0321 0.0195 0.0156 0.0573 0.0629 0.9732 0.6377 0.0487 0.00 0.1034 0.1667 0.2083 0.0320 0.0428 0.0313 0.0194 0.0151 reg oper account block of flats 0.0396 Stone, brick No 0.0 0.0 0.0 0.0 -1689.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 2.0
105 100124 0 Cash loans M N Y 0 225000.000 448272.0 28647.0 396000.0 Unaccompanied Pensioner Secondary / secondary special Widow House / apartment 0.072508 -24051 365243 -7040.0 -4053 NaN 1 0 0 1 1 1 NaN 1.0 1 1 WEDNESDAY 17 0 0 0 0 0 0 XNA NaN 0.710318 0.554947 0.2959 0.1785 0.9786 0.7076 0.0000 0.3200 0.2759 0.3333 0.3750 0.0000 0.2412 0.2693 0.0000 0.0061 0.3015 0.1853 0.9786 0.7190 0.0000 0.3222 0.2759 0.3333 0.3750 0.0000 0.2635 0.2805 0.0000 0.0064 0.2987 0.1785 0.9786 0.7115 0.0000 0.32 0.2759 0.3333 0.3750 0.0000 0.2454 0.2741 0.0000 0.0062 reg oper account block of flats 0.2131 Panel No 0.0 0.0 0.0 0.0 -1080.0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 1.0 8.0
106 100125 0 Cash loans F N N 0 126000.000 263686.5 26208.0 238500.0 Unaccompanied Pensioner Secondary / secondary special Married House / apartment 0.009549 -21613 365243 -2376.0 -2434 NaN 1 0 0 1 1 0 NaN 2.0 2 2 MONDAY 11 0 0 0 0 0 0 XNA NaN 0.571331 0.554947 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 0.0 0.0 0.0 0.0 -1165.0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 2.0
107 100126 0 Revolving loans M N Y 2 135000.000 180000.0 9000.0 180000.0 Family Working Secondary / secondary special Married House / apartment 0.046220 -12900 -782 -2124.0 -3888 NaN 1 1 1 1 0 0 NaN 4.0 1 1 FRIDAY 21 0 0 0 0 1 1 Transport: type 4 NaN 0.705093 0.574447 0.0165 0.0309 0.9732 NaN NaN NaN 0.0690 0.0417 NaN NaN NaN 0.0126 NaN NaN 0.0168 0.0321 0.9732 NaN NaN NaN 0.0690 0.0417 NaN NaN NaN 0.0131 NaN NaN 0.0167 0.0309 0.9732 NaN NaN NaN 0.0690 0.0417 NaN NaN NaN 0.0128 NaN NaN NaN block of flats 0.0099 Stone, brick No 1.0 1.0 1.0 1.0 -1091.0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 0.0
108 100127 0 Cash loans M N Y 0 112500.000 52128.0 5341.5 45000.0 Unaccompanied Pensioner Secondary / secondary special Married House / apartment 0.024610 -21341 365243 -10150.0 -1403 NaN 1 0 0 1 1 0 NaN 2.0 2 2 FRIDAY 7 0 0 0 0 0 0 XNA NaN 0.687003 NaN 0.0742 0.0000 0.9861 0.8096 NaN 0.0800 0.0690 0.3333 0.0417 0.0435 0.0605 0.0736 0.0000 0.0000 0.0756 0.0000 0.9861 0.8171 NaN 0.0806 0.0690 0.3333 0.0417 0.0445 0.0661 0.0766 0.0000 0.0000 0.0749 0.0000 0.9861 0.8121 NaN 0.08 0.0690 0.3333 0.0417 0.0443 0.0616 0.0749 0.0000 0.0000 reg oper account block of flats 0.0766 Panel No 0.0 0.0 0.0 0.0 -2279.0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NaN NaN NaN NaN NaN NaN
109 100129 0 Revolving loans F Y Y 1 85500.000 135000.0 6750.0 135000.0 Unaccompanied Working Higher education Married House / apartment 0.018029 -9415 -225 -4066.0 -2104 5.0 1 1 0 0 0 0 NaN 3.0 3 2 MONDAY 8 0 0 0 0 0 0 Business Entity Type 1 0.479544 0.610207 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 0.0 0.0 0.0 0.0 0.0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1.0 0.0 0.0 0.0 0.0 0.0
110 100130 1 Cash loans F N Y 1 157500.000 723996.0 30802.5 585000.0 Unaccompanied Commercial associate Incomplete higher Separated House / apartment 0.007274 -10526 -267 -387.0 -3190 NaN 1 1 0 1 1 0 Sales staff 2.0 2 2 SUNDAY 12 0 1 1 0 1 1 Trade: type 2 NaN 0.282069 0.120641 0.1485 0.0604 0.9881 0.8368 0.0882 0.0400 0.0345 0.3333 0.3750 0.1368 0.1202 0.1012 0.0039 0.0027 0.1513 0.0627 0.9881 0.8432 0.0890 0.0403 0.0345 0.3333 0.3750 0.1399 0.1313 0.1055 0.0039 0.0029 0.1499 0.0604 0.9881 0.8390 0.0888 0.04 0.0345 0.3333 0.3750 0.1392 0.1223 0.1030 0.0039 0.0028 reg oper account block of flats 0.1284 Stone, brick No 0.0 0.0 0.0 0.0 -56.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 0.0
111 100131 0 Cash loans F N Y 0 270000.000 891072.0 45625.5 720000.0 Unaccompanied Commercial associate Secondary / secondary special Single / not married House / apartment 0.072508 -13830 -1169 -7953.0 -4802 NaN 1 1 0 1 0 0 Realty agents 1.0 1 1 THURSDAY 15 0 0 0 0 0 0 Other 0.339403 0.707972 0.452534 0.1423 0.0785 0.9841 0.7824 0.2632 0.2400 0.1034 0.4583 0.5000 0.0216 0.1110 0.1627 0.0232 0.0479 0.1450 0.0814 0.9841 0.7909 0.2656 0.2417 0.1034 0.4583 0.5000 0.0221 0.1212 0.1695 0.0233 0.0507 0.1436 0.0785 0.9841 0.7853 0.2649 0.24 0.1034 0.4583 0.5000 0.0220 0.1129 0.1656 0.0233 0.0489 reg oper account block of flats 0.1439 Panel No 2.0 1.0 2.0 1.0 -734.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 4.0
112 100132 0 Cash loans F N Y 0 202500.000 1136439.0 73611.0 1089000.0 Unaccompanied Commercial associate Secondary / secondary special Widow House / apartment 0.032561 -23656 -968 -14666.0 -4456 NaN 1 1 0 1 1 0 Cleaning staff 1.0 1 1 TUESDAY 14 0 0 0 0 0 0 Business Entity Type 2 0.774880 0.711289 0.597192 0.3320 NaN 0.9801 NaN NaN 0.3600 0.3103 0.3333 NaN 0.2208 NaN 0.3263 NaN 0.0478 0.3382 NaN 0.9801 NaN NaN 0.3625 0.3103 0.3333 NaN 0.2259 NaN 0.3400 NaN 0.0506 0.3352 NaN 0.9801 NaN NaN 0.36 0.3103 0.3333 NaN 0.2247 NaN 0.3322 NaN 0.0488 NaN block of flats 0.2670 Panel No 0.0 0.0 0.0 0.0 -905.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 1.0
113 100133 0 Cash loans F N N 0 67500.000 384048.0 18810.0 270000.0 Unaccompanied Working Secondary / secondary special Civil marriage With parents 0.002134 -9207 -1043 -4069.0 -1882 NaN 1 1 0 1 0 0 Core staff 2.0 3 3 WEDNESDAY 10 0 0 0 0 1 1 Government 0.548647 0.316755 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 0.0 0.0 0.0 0.0 -591.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NaN NaN NaN NaN NaN NaN
114 100134 0 Revolving loans F N N 0 202500.000 180000.0 9000.0 180000.0 Unaccompanied Commercial associate Secondary / secondary special Married House / apartment 0.018634 -10085 -465 -9649.0 -1666 NaN 1 1 0 0 0 0 Secretaries 2.0 2 2 MONDAY 17 0 0 0 0 0 0 Medicine 0.329186 0.749241 0.694093 NaN NaN 0.9816 NaN NaN NaN NaN NaN NaN NaN NaN 0.2365 NaN NaN NaN NaN 0.9816 NaN NaN NaN NaN NaN NaN NaN NaN 0.2464 NaN NaN NaN NaN 0.9816 NaN NaN NaN NaN NaN NaN NaN NaN 0.2407 NaN NaN NaN NaN 0.2688 NaN No 4.0 0.0 4.0 0.0 0.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 0.0
115 100135 0 Cash loans M N Y 0 135000.000 391194.0 26271.0 319500.0 Unaccompanied Commercial associate Secondary / secondary special Married House / apartment 0.007330 -19829 -215 -4730.0 -3368 NaN 1 1 0 1 0 0 Laborers 2.0 2 2 SATURDAY 10 1 1 0 1 1 0 Business Entity Type 3 NaN 0.720640 0.320163 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 0.0 0.0 0.0 0.0 -1209.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 5.0
116 100136 0 Cash loans M Y N 1 157500.000 1571931.0 49356.0 1435500.0 Unaccompanied State servant Secondary / secondary special Married House / apartment 0.046220 -15949 -239 -1146.0 -4321 9.0 1 1 0 1 1 1 Laborers 3.0 1 1 SUNDAY 13 0 0 0 0 0 0 Business Entity Type 3 NaN 0.682017 NaN 0.5351 0.0118 0.9980 0.9728 0.2781 0.6800 0.2759 0.6667 0.5000 0.0749 0.4194 0.5357 0.0772 0.5209 0.5452 0.0122 0.9980 0.9739 0.2807 0.6848 0.2759 0.6667 0.5000 0.0766 0.4582 0.5582 0.0778 0.5514 0.5402 0.0118 0.9980 0.9732 0.2799 0.68 0.2759 0.6667 0.5000 0.0762 0.4267 0.5453 0.0776 0.5318 reg oper spec account block of flats 0.7010 Monolithic No 0.0 0.0 0.0 0.0 -2397.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 1.0
117 100137 0 Cash loans F Y Y 0 225000.000 891072.0 45625.5 720000.0 Family Pensioner Secondary / secondary special Married House / apartment 0.020246 -20284 365243 -1990.0 -2556 2.0 1 0 0 1 0 0 NaN 2.0 3 3 FRIDAY 8 0 0 0 0 0 0 XNA 0.886487 0.086719 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 12.0 0.0 12.0 0.0 0.0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NaN NaN NaN NaN NaN NaN
118 100138 0 Cash loans M Y N 0 135000.000 824823.0 29223.0 688500.0 Unaccompanied Pensioner Higher education Married House / apartment 0.046220 -14339 365243 -1104.0 -4620 5.0 1 0 0 1 0 0 NaN 2.0 1 1 TUESDAY 14 0 0 0 0 0 0 XNA 0.667496 0.607562 0.572683 0.3247 NaN NaN NaN NaN 0.5600 0.2414 0.6250 NaN NaN NaN 0.4549 NaN 0.0708 0.3309 NaN NaN NaN NaN 0.5639 0.2414 0.6250 NaN NaN NaN 0.4739 NaN 0.0749 0.3279 NaN NaN NaN NaN 0.56 0.2414 0.6250 NaN NaN NaN 0.4630 NaN 0.0722 NaN block of flats 0.4810 Panel No 0.0 0.0 0.0 0.0 -242.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 1.0 0.0 0.0
119 100139 0 Cash loans F N N 1 157500.000 302341.5 24016.5 261000.0 Family Working Secondary / secondary special Married House / apartment 0.010276 -13286 -2305 -6685.0 -4955 NaN 1 1 0 1 0 1 NaN 3.0 2 2 WEDNESDAY 10 0 0 0 1 1 0 Business Entity Type 3 0.567775 0.601408 0.096319 0.0897 0.1127 0.9866 0.8164 0.0280 0.0000 0.2069 0.1667 0.2083 0.0406 0.0731 0.0898 0.0000 0.0000 0.0914 0.1170 0.9866 0.8236 0.0283 0.0000 0.2069 0.1667 0.2083 0.0416 0.0799 0.0936 0.0000 0.0000 0.0906 0.1127 0.9866 0.8189 0.0282 0.00 0.2069 0.1667 0.2083 0.0413 0.0744 0.0915 0.0000 0.0000 reg oper spec account block of flats 0.0860 Panel No 6.0 0.0 6.0 0.0 -1868.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 1.0 0.0 0.0 3.0
120 100140 0 Revolving loans F N N 0 157500.000 675000.0 33750.0 675000.0 Unaccompanied Commercial associate Higher education Married House / apartment 0.030755 -9223 -802 -4067.0 -505 NaN 1 1 0 1 0 0 High skill tech staff 2.0 2 2 FRIDAY 9 0 0 0 1 1 0 Business Entity Type 3 0.370342 0.715861 NaN 0.0515 0.0557 0.9851 0.7960 0.0270 0.0000 0.1379 0.1667 0.0417 0.0518 0.0420 0.0618 0.1931 0.0000 0.0525 0.0578 0.9851 0.8040 0.0272 0.0000 0.1379 0.1667 0.0417 0.0529 0.0459 0.0643 0.1946 0.0000 0.0520 0.0557 0.9851 0.7987 0.0271 0.00 0.1379 0.1667 0.0417 0.0527 0.0428 0.0629 0.1941 0.0000 reg oper account block of flats 0.0633 Stone, brick No 0.0 0.0 0.0 0.0 -120.0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NaN NaN NaN NaN NaN NaN
121 100142 0 Cash loans M Y Y 1 360000.000 579195.0 27994.5 468000.0 Spouse, partner State servant Higher education Civil marriage House / apartment 0.018801 -13379 -5387 -6869.0 -4540 7.0 1 1 0 1 0 0 High skill tech staff 3.0 2 2 FRIDAY 11 0 0 0 0 1 1 Transport: type 2 NaN 0.064858 0.309275 0.0918 0.0000 0.9836 NaN NaN 0.0000 0.2069 0.1667 NaN 0.0000 NaN 0.0788 NaN 0.0308 0.0935 0.0000 0.9836 NaN NaN 0.0000 0.2069 0.1667 NaN 0.0000 NaN 0.0821 NaN 0.0326 0.0926 0.0000 0.9836 NaN NaN 0.00 0.2069 0.1667 NaN 0.0000 NaN 0.0802 NaN 0.0314 NaN block of flats 0.0686 Panel No 2.0 0.0 1.0 0.0 -1759.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 0.0
122 100143 0 Revolving loans M N Y 0 162000.000 270000.0 13500.0 270000.0 Unaccompanied State servant Higher education Married House / apartment 0.035792 -22881 -3420 -1481.0 -4802 NaN 1 1 0 1 0 0 Core staff 2.0 2 2 TUESDAY 10 0 0 0 0 0 0 Government 0.659404 0.678125 0.683269 0.1732 0.0785 0.9940 NaN NaN 0.2400 0.1034 0.4583 NaN 0.2983 NaN 0.1974 NaN 0.0266 0.1765 0.0815 0.9940 NaN NaN 0.2417 0.1034 0.4583 NaN 0.3051 NaN 0.2056 NaN 0.0281 0.1749 0.0785 0.9940 NaN NaN 0.24 0.1034 0.4583 NaN 0.3035 NaN 0.2009 NaN 0.0271 NaN block of flats 0.1610 Block No 0.0 0.0 0.0 0.0 -1499.0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 4.0
123 100144 0 Cash loans F N N 0 112500.000 535500.0 30028.5 535500.0 Unaccompanied Working Secondary / secondary special Separated House / apartment 0.028663 -16759 -4560 -3079.0 -296 NaN 1 1 0 1 0 0 Sales staff 1.0 2 2 THURSDAY 17 0 0 0 0 0 0 Self-employed NaN 0.611637 NaN 0.0845 0.0651 0.9757 0.6668 0.0506 0.0000 0.1379 0.1667 0.2083 0.0421 0.0605 0.0641 0.0386 0.0241 0.0861 0.0675 0.9757 0.6798 0.0510 0.0000 0.1379 0.1667 0.2083 0.0430 0.0661 0.0668 0.0389 0.0255 0.0854 0.0651 0.9757 0.6713 0.0509 0.00 0.1379 0.1667 0.2083 0.0428 0.0616 0.0652 0.0388 0.0246 reg oper account block of flats 0.0833 Panel No 9.0 2.0 9.0 0.0 -1282.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NaN NaN NaN NaN NaN NaN
124 100145 0 Cash loans F Y Y 1 202500.000 260725.5 16789.5 198000.0 Family Working Secondary / secondary special Separated House / apartment 0.018850 -16282 -4375 -762.0 -1494 8.0 1 1 0 1 0 0 Laborers 2.0 2 2 TUESDAY 11 0 0 0 0 0 0 Self-employed 0.647045 0.746486 0.739412 0.0928 0.1000 0.9801 0.7280 0.0463 0.0000 0.2069 0.1667 0.2083 0.0437 0.0756 0.0903 0.0000 0.0000 0.0945 0.1038 0.9801 0.7387 0.0467 0.0000 0.2069 0.1667 0.2083 0.0447 0.0826 0.0941 0.0000 0.0000 0.0937 0.1000 0.9801 0.7316 0.0466 0.00 0.2069 0.1667 0.2083 0.0445 0.0770 0.0919 0.0000 0.0000 reg oper spec account block of flats 0.0710 Panel No 6.0 0.0 6.0 0.0 -1782.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 3.0
125 100146 0 Cash loans M Y Y 0 117000.000 327024.0 12456.0 270000.0 Unaccompanied Pensioner Secondary / secondary special Married House / apartment 0.007330 -22959 365243 -1730.0 -4047 13.0 1 0 0 1 0 0 NaN 2.0 2 2 WEDNESDAY 9 0 0 0 0 0 0 XNA NaN 0.282326 0.755740 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 2.0 0.0 2.0 0.0 -909.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 1.0
126 100147 0 Cash loans F N Y 1 135000.000 518562.0 22099.5 463500.0 Unaccompanied Working Secondary / secondary special Married House / apartment 0.003813 -16142 -2832 -3440.0 -3970 NaN 1 1 0 1 0 0 Laborers 3.0 2 2 SUNDAY 7 0 0 0 0 0 0 Business Entity Type 3 0.684696 0.410907 0.738020 0.1144 0.0775 0.9826 NaN NaN 0.0000 0.1379 0.1667 NaN 0.0630 NaN 0.0654 NaN 0.0242 0.1166 0.0804 0.9826 NaN NaN 0.0000 0.1379 0.1667 NaN 0.0644 NaN 0.0681 NaN 0.0256 0.1155 0.0775 0.9826 NaN NaN 0.00 0.1379 0.1667 NaN 0.0641 NaN 0.0666 NaN 0.0247 NaN block of flats 0.0567 Panel No 1.0 0.0 1.0 0.0 -1198.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 1.0 4.0
127 100148 0 Cash loans F N Y 0 247500.000 755190.0 36459.0 675000.0 Family Commercial associate Secondary / secondary special Married House / apartment 0.008575 -16340 -8647 -4182.0 -4259 NaN 1 1 0 1 0 0 Medicine staff 2.0 2 2 MONDAY 17 0 0 0 0 0 0 Other 0.617716 0.660559 0.211551 0.0495 0.0623 0.9737 NaN NaN 0.0000 0.1034 0.1250 NaN 0.0486 NaN NaN NaN NaN 0.0504 0.0646 0.9737 NaN NaN 0.0000 0.1034 0.1250 NaN 0.0497 NaN NaN NaN NaN 0.0500 0.0623 0.9737 NaN NaN 0.00 0.1034 0.1250 NaN 0.0495 NaN NaN NaN NaN NaN block of flats 0.0318 Stone, brick No 0.0 0.0 0.0 0.0 -2415.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 0.0
128 100149 0 Cash loans M Y N 0 171000.000 539590.5 26374.5 445500.0 Family Working Secondary / secondary special Married House / apartment 0.030755 -9203 -481 -1642.0 -1639 4.0 1 1 0 1 0 0 Low-skill Laborers 2.0 2 2 TUESDAY 17 0 0 0 0 0 0 Self-employed 0.053183 0.164463 0.092013 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 1.0 0.0 1.0 0.0 -395.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 0.0
129 100151 0 Cash loans M N N 0 135000.000 182016.0 11772.0 144000.0 Unaccompanied Commercial associate Higher education Single / not married House / apartment 0.019101 -16522 -1451 -4101.0 -66 NaN 1 1 0 1 0 0 Accountants 1.0 2 2 MONDAY 10 0 0 0 0 0 0 Business Entity Type 3 NaN 0.695219 0.729567 0.0567 0.0331 0.9791 0.7144 0.0208 0.0400 0.0345 0.3333 0.3750 0.0256 0.0454 0.0434 0.0039 0.0072 0.0578 0.0343 0.9791 0.7256 0.0210 0.0403 0.0345 0.3333 0.3750 0.0262 0.0496 0.0452 0.0039 0.0076 0.0573 0.0331 0.9791 0.7182 0.0209 0.04 0.0345 0.3333 0.3750 0.0261 0.0462 0.0442 0.0039 0.0073 reg oper account block of flats 0.0471 Stone, brick No 0.0 0.0 0.0 0.0 -398.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 1.0 1.0
130 100152 0 Cash loans F N Y 0 157500.000 254412.0 9720.0 166500.0 Unaccompanied Working Secondary / secondary special Married Municipal apartment 0.010032 -16083 -2764 -7358.0 -4654 NaN 1 1 0 1 0 0 Sales staff 2.0 2 2 WEDNESDAY 11 0 0 0 0 0 0 Self-employed NaN 0.165442 0.749022 0.0742 0.0457 0.9821 0.7552 0.0000 0.0800 0.0690 0.3333 0.3750 0.0000 0.0605 0.0783 0.0000 0.0000 0.0756 0.0474 0.9821 0.7648 0.0000 0.0806 0.0690 0.3333 0.3750 0.0000 0.0661 0.0815 0.0000 0.0000 0.0749 0.0457 0.9821 0.7585 0.0000 0.08 0.0690 0.3333 0.3750 0.0000 0.0616 0.0797 0.0000 0.0000 reg oper account block of flats 0.0752 Panel No 0.0 0.0 0.0 0.0 -1761.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 1.0 4.0
131 100153 0 Cash loans M Y Y 0 202500.000 1107981.0 32526.0 967500.0 Family Working Secondary / secondary special Married House / apartment 0.031329 -18413 -1895 -10826.0 -1947 15.0 1 1 0 1 0 0 Drivers 2.0 2 2 SATURDAY 10 0 0 0 0 0 0 Construction NaN 0.668020 0.353988 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 0.0 0.0 0.0 0.0 -1192.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 5.0
132 100154 0 Revolving loans M N N 0 144000.000 180000.0 9000.0 180000.0 Spouse, partner Working Secondary / secondary special Married House / apartment 0.030755 -12157 -2995 -4055.0 -267 NaN 1 1 0 1 0 0 Drivers 2.0 2 2 THURSDAY 11 0 0 0 0 0 0 Self-employed 0.127393 0.494962 0.738020 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 0.0 0.0 0.0 0.0 -329.0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 1.0
133 100155 0 Cash loans M N N 0 157500.000 675000.0 21906.0 675000.0 Unaccompanied Working Secondary / secondary special Married House / apartment 0.015221 -11944 -2899 -5812.0 -2722 NaN 1 1 1 1 1 0 Laborers 2.0 2 2 THURSDAY 11 0 1 1 0 1 1 Construction NaN 0.282078 0.565608 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 0.0 0.0 0.0 0.0 -524.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 0.0
134 100156 0 Cash loans F Y N 2 157500.000 945000.0 39127.5 945000.0 Family Commercial associate Secondary / secondary special Married With parents 0.018209 -14235 -5411 -2248.0 -5376 65.0 1 1 0 1 0 0 Medicine staff 4.0 3 3 THURSDAY 14 0 0 0 0 0 0 Medicine NaN 0.597823 0.746300 0.0289 0.0698 0.9682 0.5648 0.0044 0.0000 0.1034 0.1250 0.1250 0.0437 0.0202 0.0264 0.0154 0.0326 0.0294 0.0725 0.9682 0.5818 0.0045 0.0000 0.1034 0.1250 0.1250 0.0447 0.0220 0.0275 0.0156 0.0345 0.0291 0.0698 0.9682 0.5706 0.0045 0.00 0.1034 0.1250 0.1250 0.0444 0.0205 0.0269 0.0155 0.0333 reg oper account block of flats 0.0278 Stone, brick No 0.0 0.0 0.0 0.0 -2703.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 1.0 0.0 1.0
135 100157 0 Cash loans M N Y 0 180000.000 1755000.0 61132.5 1755000.0 Unaccompanied Working Secondary / secondary special Married House / apartment 0.014520 -16086 -1549 -3174.0 -4584 NaN 1 1 0 1 0 0 NaN 2.0 2 2 MONDAY 7 0 0 0 0 0 0 Business Entity Type 3 0.867872 0.147685 0.669057 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 3.0 0.0 3.0 0.0 0.0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 1.0 0.0 2.0
136 100158 0 Cash loans M N N 0 180000.000 590337.0 27486.0 477000.0 Unaccompanied Commercial associate Incomplete higher Married House / apartment 0.022800 -12348 -1821 -1909.0 -4800 NaN 1 1 0 1 1 0 Core staff 2.0 2 2 WEDNESDAY 12 0 0 0 0 0 0 Culture NaN 0.683703 NaN 0.1515 0.0969 0.9950 0.9252 0.0289 0.1332 0.1148 0.3750 0.4167 0.0184 0.1219 0.1517 0.0077 0.0260 0.1345 0.0659 0.9926 0.9020 0.0292 0.1208 0.1034 0.3750 0.4167 0.0009 0.1166 0.1351 0.0000 0.0000 0.1405 0.1091 0.9945 0.9262 0.0291 0.12 0.1034 0.3750 0.4167 0.0009 0.1154 0.1587 0.0039 0.0121 reg oper account block of flats 0.1178 Stone, brick No 0.0 0.0 0.0 0.0 -2041.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 1.0
137 100159 0 Cash loans F N Y 0 225000.000 1515415.5 41800.5 1354500.0 Unaccompanied Commercial associate Secondary / secondary special Married House / apartment 0.003818 -14645 -4027 -7154.0 -1969 NaN 1 1 1 1 1 0 Sales staff 2.0 2 2 FRIDAY 3 0 0 0 0 0 0 Trade: type 7 0.545859 0.571250 0.789880 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 2.0 0.0 2.0 0.0 -700.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 2.0 0.0
138 100160 1 Cash loans M N Y 0 292500.000 675000.0 36747.0 675000.0 Unaccompanied Working Higher education Married House / apartment 0.035792 -16667 -200 -5239.0 -114 NaN 1 1 1 1 1 0 Managers 2.0 2 2 FRIDAY 18 1 1 0 1 1 0 Business Entity Type 3 0.320322 0.631961 0.289611 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 0.0 0.0 0.0 0.0 -106.0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 0.0
139 100161 0 Cash loans F N Y 0 90000.000 370629.0 13972.5 306000.0 Family Commercial associate Secondary / secondary special Married House / apartment 0.007120 -16219 -2599 -9329.0 -4325 NaN 1 1 0 1 0 0 Sales staff 2.0 2 2 MONDAY 9 0 0 0 0 1 1 Self-employed NaN 0.265312 0.317032 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 0.0 0.0 0.0 0.0 -306.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 0.0
140 100162 0 Cash loans F N Y 0 112500.000 547344.0 18216.0 472500.0 Family Pensioner Secondary / secondary special Single / not married House / apartment 0.020246 -21061 365243 -5439.0 -4488 NaN 1 0 0 1 0 0 NaN 1.0 3 3 MONDAY 6 0 0 0 0 0 0 XNA 0.593567 0.598243 0.232725 0.0907 0.0992 0.9771 0.6872 0.0000 0.0000 0.2069 0.1667 0.2083 0.0456 0.0735 0.0854 0.0019 0.0030 0.0903 0.1016 0.9772 0.6994 0.0000 0.0000 0.2069 0.1667 0.2083 0.0419 0.0781 0.0870 0.0000 0.0000 0.0916 0.0992 0.9771 0.6914 0.0000 0.00 0.2069 0.1667 0.2083 0.0464 0.0748 0.0870 0.0019 0.0030 reg oper account block of flats 0.0669 Panel No 1.0 1.0 1.0 1.0 -1594.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 2.0
141 100163 0 Cash loans F N Y 0 130500.000 550489.5 19773.0 454500.0 Unaccompanied Working Higher education Separated House / apartment 0.018209 -19718 -5717 -8940.0 -3200 NaN 1 1 1 1 1 0 NaN 1.0 3 3 FRIDAY 11 0 0 0 0 0 0 Other 0.656938 0.390993 0.095070 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 5.0 0.0 5.0 0.0 -1691.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 2.0
142 100164 0 Cash loans F Y N 1 450000.000 407434.5 43362.0 387000.0 Unaccompanied Commercial associate Higher education Civil marriage House / apartment 0.010500 -16097 -2630 -110.0 -3852 3.0 1 1 0 1 0 0 Core staff 3.0 3 3 WEDNESDAY 17 0 0 0 0 0 0 Self-employed NaN 0.622030 0.328063 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 0.0 0.0 0.0 0.0 -845.0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 0.0
143 100165 0 Cash loans F Y Y 0 175500.000 1293502.5 35568.0 1129500.0 Family Pensioner Secondary / secondary special Married House / apartment 0.018850 -21961 365243 -8887.0 -4379 9.0 1 0 0 1 1 0 NaN 2.0 2 2 TUESDAY 16 0 0 0 0 0 0 XNA 0.531106 0.646689 0.812823 0.0680 0.0730 0.9876 0.8300 0.0283 0.0000 0.1724 0.1667 0.1250 0.0366 0.0546 0.0714 0.0039 0.0094 0.0693 0.0758 0.9876 0.8367 0.0286 0.0000 0.1724 0.1667 0.1250 0.0374 0.0597 0.0744 0.0039 0.0100 0.0687 0.0730 0.9876 0.8323 0.0285 0.00 0.1724 0.1667 0.1250 0.0372 0.0556 0.0727 0.0039 0.0096 reg oper account block of flats 0.0737 Panel No 0.0 0.0 0.0 0.0 -396.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 2.0
144 100166 0 Cash loans F N Y 3 58500.000 152820.0 15241.5 135000.0 Unaccompanied Working Secondary / secondary special Married Municipal apartment 0.025164 -14359 -1894 -2908.0 -4480 NaN 1 1 1 1 0 0 Core staff 5.0 2 2 FRIDAY 8 0 0 0 0 1 1 Government NaN 0.524818 0.156640 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 2.0 0.0 2.0 0.0 -160.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 4.0
145 100167 0 Cash loans F N Y 0 180000.000 427500.0 46170.0 427500.0 Unaccompanied Commercial associate Secondary / secondary special Civil marriage House / apartment 0.026392 -15127 -1039 -4221.0 -652 NaN 1 1 0 1 1 0 NaN 2.0 2 2 FRIDAY 16 0 0 0 0 1 1 Hotel NaN 0.551308 0.161463 0.0082 NaN 0.9732 NaN NaN 0.0000 0.0690 0.0417 NaN NaN NaN 0.0084 NaN 0.0000 0.0084 NaN 0.9732 NaN NaN 0.0000 0.0690 0.0417 NaN NaN NaN 0.0088 NaN 0.0000 0.0083 NaN 0.9732 NaN NaN 0.00 0.0690 0.0417 NaN NaN NaN 0.0086 NaN 0.0000 NaN block of flats 0.0066 Stone, brick No 0.0 0.0 0.0 0.0 -1914.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 1.0
146 100173 0 Cash loans F N N 0 117000.000 781920.0 26725.5 675000.0 Unaccompanied Working Higher education Single / not married House / apartment 0.024610 -11633 -2596 -5430.0 -3851 NaN 1 1 0 1 1 0 Core staff 1.0 2 2 TUESDAY 17 0 0 0 0 0 0 Government 0.723940 0.636395 0.452534 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN No 1.0 0.0 1.0 0.0 -1166.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 1.0
147 100174 0 Revolving loans F N Y 2 180000.000 225000.0 11250.0 225000.0 Spouse, partner Working Incomplete higher Married Municipal apartment 0.032561 -14597 -1997 -8491.0 -3681 NaN 1 1 1 1 1 0 Sales staff 4.0 1 1 FRIDAY 15 0 0 0 0 0 0 Self-employed NaN 0.704793 0.565608 0.0557 NaN 0.9752 NaN NaN 0.0400 0.0345 0.3333 NaN 0.0361 NaN 0.0383 NaN 0.0028 0.0567 NaN 0.9752 NaN NaN 0.0403 0.0345 0.3333 NaN 0.0369 NaN 0.0400 NaN 0.0030 0.0562 NaN 0.9752 NaN NaN 0.04 0.0345 0.3333 NaN 0.0367 NaN 0.0390 NaN 0.0029 NaN NaN 0.0308 Panel No 0.0 0.0 0.0 0.0 -307.0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 0.0
148 100175 0 Cash loans F N Y 1 202500.000 1251072.0 45067.5 1080000.0 Unaccompanied Working Secondary / secondary special Civil marriage House / apartment 0.010147 -16296 -266 -164.0 -4568 NaN 1 1 0 1 0 0 NaN 3.0 2 2 TUESDAY 19 0 0 0 0 0 0 Industry: type 7 0.580367 0.763649 0.329655 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 0.0 0.0 0.0 0.0 -1683.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 1.0 0.0 0.0
149 100176 0 Cash loans F N Y 0 135000.000 1078200.0 31653.0 900000.0 Unaccompanied Working Secondary / secondary special Married House / apartment 0.010006 -17682 -4319 -2630.0 -1211 NaN 1 1 0 1 1 0 Laborers 2.0 2 2 WEDNESDAY 6 0 0 0 0 0 0 Government NaN 0.585291 0.816092 0.0701 0.0533 0.9960 0.9456 0.0180 0.0800 0.0690 0.3333 0.3750 0.0096 0.0555 0.0700 0.0077 0.0164 0.0714 0.0553 0.9960 0.9477 0.0182 0.0806 0.0690 0.3333 0.3750 0.0098 0.0606 0.0729 0.0078 0.0174 0.0708 0.0533 0.9960 0.9463 0.0181 0.08 0.0690 0.3333 0.3750 0.0098 0.0564 0.0713 0.0078 0.0168 reg oper account block of flats 0.0685 Panel No 1.0 0.0 1.0 0.0 -131.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 1.0
150 100177 0 Cash loans M N N 1 112500.000 312682.5 33309.0 297000.0 Family Working Secondary / secondary special Civil marriage House / apartment 0.010643 -14311 -334 -8401.0 -4248 NaN 1 1 1 1 0 0 NaN 3.0 2 2 THURSDAY 10 0 1 1 0 1 1 Self-employed 0.319357 0.603655 0.595456 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 0.0 0.0 0.0 0.0 -328.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 0.0
151 100178 0 Cash loans F N Y 0 119250.000 679500.0 28917.0 679500.0 Unaccompanied Working Higher education Separated House / apartment 0.008866 -19286 -1485 -7887.0 -2835 NaN 1 1 1 1 1 0 NaN 1.0 2 2 MONDAY 13 0 0 0 0 0 0 Self-employed 0.723371 0.765125 0.417100 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 0.0 0.0 0.0 0.0 -2085.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 0.0
152 100179 0 Cash loans F Y N 0 202500.000 675000.0 53329.5 675000.0 Unaccompanied Commercial associate Higher education Married House / apartment 0.031329 -11375 -2311 -180.0 -2009 4.0 1 1 0 1 0 0 Managers 2.0 2 2 MONDAY 13 0 0 0 0 0 0 Trade: type 7 0.674832 0.616156 0.673830 0.5845 0.4570 0.9945 0.9252 0.3468 0.6000 0.2414 0.9167 0.5000 0.2847 0.4287 0.7187 0.2201 0.7729 0.5956 0.4742 0.9945 0.9281 0.3500 0.6042 0.2414 0.9167 0.5000 0.2912 0.4683 0.7488 0.2218 0.8183 0.5902 0.4570 0.9945 0.9262 0.3490 0.60 0.2414 0.9167 0.5000 0.2896 0.4361 0.7317 0.2213 0.7892 reg oper account block of flats 0.7334 Monolithic No 0.0 0.0 0.0 0.0 -1792.0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 1.0 0.0 4.0
153 100180 0 Cash loans F N Y 1 315000.000 1288350.0 37800.0 1125000.0 Unaccompanied Commercial associate Higher education Married House / apartment 0.020713 -15581 -4940 -9520.0 -5239 NaN 1 1 0 1 0 0 Accountants 3.0 3 2 FRIDAY 15 0 0 0 0 1 1 Transport: type 4 0.729090 0.509852 0.675413 0.0907 0.1072 0.9811 0.7416 0.0189 0.0000 0.2069 0.1667 0.2083 0.2668 0.0740 0.0839 0.0000 0.0000 0.0924 0.1113 0.9811 0.7517 0.0190 0.0000 0.2069 0.1667 0.2083 0.2729 0.0808 0.0874 0.0000 0.0000 0.0916 0.1072 0.9811 0.7451 0.0190 0.00 0.2069 0.1667 0.2083 0.2715 0.0752 0.0854 0.0000 0.0000 reg oper account block of flats 0.0763 Panel No 1.0 0.0 1.0 0.0 -1020.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 2.0 0.0 3.0
154 100181 1 Cash loans F N Y 0 157500.000 245619.0 12667.5 166500.0 Unaccompanied Working Secondary / secondary special Single / not married House / apartment 0.022625 -17538 -7676 -774.0 -1082 NaN 1 1 0 1 1 0 Private service staff 1.0 2 2 TUESDAY 9 0 0 0 0 0 0 Business Entity Type 3 NaN 0.182171 0.443615 0.0660 NaN 0.9757 NaN NaN 0.0000 0.1379 0.1667 NaN 0.0460 NaN 0.0519 NaN NaN 0.0672 NaN 0.9757 NaN NaN 0.0000 0.1379 0.1667 NaN 0.0471 NaN 0.0541 NaN NaN 0.0666 NaN 0.9757 NaN NaN 0.00 0.1379 0.1667 NaN 0.0468 NaN 0.0529 NaN NaN NaN block of flats 0.0552 Block No 0.0 0.0 0.0 0.0 -216.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 6.0
155 100182 0 Revolving loans F N Y 1 112500.000 135000.0 6750.0 135000.0 Unaccompanied Working Secondary / secondary special Separated House / apartment 0.020246 -13558 -4758 -6426.0 -4360 NaN 1 1 0 1 0 0 Core staff 2.0 3 3 THURSDAY 8 0 0 0 0 0 0 Government 0.464356 0.751869 0.420611 0.0670 0.0806 0.9747 0.6532 0.0086 0.0000 0.1379 0.1667 0.2083 0.0547 0.0538 0.0332 0.0039 0.1078 0.0683 0.0836 0.9747 0.6668 0.0087 0.0000 0.1379 0.1667 0.2083 0.0560 0.0588 0.0346 0.0039 0.1141 0.0677 0.0806 0.9747 0.6578 0.0086 0.00 0.1379 0.1667 0.2083 0.0557 0.0547 0.0338 0.0039 0.1101 reg oper account block of flats 0.0496 Stone, brick No 0.0 0.0 0.0 0.0 -1933.0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 3.0
156 100183 0 Cash loans F N Y 0 103500.000 239850.0 23494.5 225000.0 Unaccompanied Pensioner Secondary / secondary special Separated House / apartment 0.018634 -24729 365243 -4047.0 -4523 NaN 1 0 0 1 1 0 NaN 1.0 2 2 THURSDAY 12 0 0 0 0 0 0 XNA NaN 0.600433 0.795803 0.0247 0.0612 0.9757 NaN NaN 0.0000 0.1034 0.0833 NaN 0.0270 NaN 0.0304 NaN 0.0131 0.0252 0.0635 0.9757 NaN NaN 0.0000 0.1034 0.0833 NaN 0.0276 NaN 0.0317 NaN 0.0139 0.0250 0.0612 0.9757 NaN NaN 0.00 0.1034 0.0833 NaN 0.0275 NaN 0.0309 NaN 0.0134 NaN block of flats 0.0267 Stone, brick No 0.0 0.0 0.0 0.0 -1183.0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 0.0
157 100185 0 Cash loans M N Y 0 58500.000 187704.0 10903.5 148500.0 Unaccompanied Pensioner Secondary / secondary special Married House / apartment 0.018029 -20215 365243 -3508.0 -3558 NaN 1 0 0 1 0 0 NaN 2.0 3 2 SATURDAY 7 0 0 0 0 0 0 XNA NaN 0.557348 0.771362 0.0619 0.0473 0.9771 0.6872 0.0228 0.0000 0.1379 0.1667 0.2083 0.0335 0.0504 0.0544 0.0000 0.0000 0.0630 0.0491 0.9772 0.6994 0.0230 0.0000 0.1379 0.1667 0.2083 0.0343 0.0551 0.0567 0.0000 0.0000 0.0625 0.0473 0.9771 0.6914 0.0230 0.00 0.1379 0.1667 0.2083 0.0341 0.0513 0.0554 0.0000 0.0000 reg oper account block of flats 0.0553 Panel No 1.0 0.0 1.0 0.0 -1792.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 5.0
158 100186 0 Cash loans F N Y 0 117000.000 269550.0 13095.0 225000.0 Unaccompanied Pensioner Secondary / secondary special Single / not married House / apartment 0.007020 -21861 365243 -830.0 -4830 NaN 1 0 0 1 1 0 NaN 1.0 2 2 TUESDAY 12 0 0 0 0 0 0 XNA NaN 0.641304 0.519097 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 4.0 0.0 4.0 0.0 -2025.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 0.0
159 100188 0 Cash loans F N N 1 225000.000 1325475.0 56290.5 1125000.0 Unaccompanied Working Higher education Married Office apartment 0.002506 -15323 -5785 -5368.0 -3602 NaN 1 1 0 1 0 0 Core staff 3.0 2 2 FRIDAY 4 0 0 0 0 1 1 School 0.728092 0.702463 0.546023 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 0.0 0.0 0.0 0.0 -573.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 1.0 0.0 1.0
160 100189 0 Cash loans F N Y 0 135000.000 263844.0 14440.5 189000.0 Unaccompanied Working Incomplete higher Separated House / apartment 0.020713 -16807 -1094 -8184.0 -339 NaN 1 1 0 1 1 0 Sales staff 1.0 3 2 THURSDAY 6 0 0 0 0 1 1 Trade: type 7 NaN 0.010272 NaN 0.0082 0.0000 0.9727 0.6260 0.0007 0.0000 0.0345 0.0417 0.0833 0.0115 0.0067 0.0054 0.0000 0.0000 0.0084 0.0000 0.9727 0.6406 0.0008 0.0000 0.0345 0.0417 0.0833 0.0117 0.0073 0.0056 0.0000 0.0000 0.0083 0.0000 0.9727 0.6310 0.0007 0.00 0.0345 0.0417 0.0833 0.0117 0.0068 0.0055 0.0000 0.0000 reg oper account block of flats 0.0046 Block No 1.0 0.0 1.0 0.0 -581.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NaN NaN NaN NaN NaN NaN
161 100190 0 Cash loans M Y N 0 162000.000 263686.5 24781.5 238500.0 Unaccompanied Commercial associate Higher education Married House / apartment 0.022625 -13972 -4472 -464.0 -4529 3.0 1 1 0 1 1 0 Laborers 2.0 2 2 THURSDAY 16 0 0 0 0 0 0 Government 0.534999 0.585859 0.788681 0.3093 0.1973 0.9891 0.8504 0.0000 0.4000 0.2414 0.4583 0.5000 0.4101 0.2522 0.3564 0.0000 0.0168 0.3151 0.2047 0.9891 0.8563 0.0000 0.4028 0.2414 0.4583 0.5000 0.4195 0.2755 0.3713 0.0000 0.0178 0.3123 0.1973 0.9891 0.8524 0.0000 0.40 0.2414 0.4583 0.5000 0.4173 0.2565 0.3628 0.0000 0.0172 reg oper account block of flats 0.4064 Panel No 5.0 0.0 5.0 0.0 -1161.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 3.0
162 100191 0 Cash loans F N Y 0 112500.000 544491.0 17563.5 454500.0 Unaccompanied Working Higher education Separated House / apartment 0.030755 -21819 -8355 -8440.0 -4474 NaN 1 1 0 1 0 0 High skill tech staff 1.0 2 2 TUESDAY 18 0 0 0 0 0 0 Business Entity Type 2 NaN 0.688792 0.522697 0.0412 0.0619 0.9762 0.6668 0.0535 0.0000 0.0690 0.1667 0.2083 0.0310 0.0336 0.0372 0.0000 0.0000 0.0420 0.0642 0.9762 0.6798 0.0540 0.0000 0.0690 0.1667 0.2083 0.0317 0.0367 0.0387 0.0000 0.0000 0.0416 0.0619 0.9762 0.6713 0.0538 0.00 0.0690 0.1667 0.2083 0.0315 0.0342 0.0378 0.0000 0.0000 reg oper spec account block of flats 0.0377 Stone, brick No 0.0 0.0 0.0 0.0 -2576.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 2.0
163 100192 1 Cash loans F N N 0 111915.000 225000.0 21037.5 225000.0 Unaccompanied Commercial associate Secondary / secondary special Single / not married With parents 0.018801 -7989 -150 -2570.0 -646 NaN 1 1 1 1 0 0 Core staff 1.0 2 2 FRIDAY 16 0 0 0 0 1 1 Trade: type 3 NaN 0.086253 0.513694 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 0.0 0.0 0.0 0.0 -122.0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 0.0
164 100193 0 Cash loans F Y N 0 225000.000 296280.0 15124.5 225000.0 Unaccompanied Commercial associate Secondary / secondary special Separated House / apartment 0.020246 -17879 -940 -2294.0 -1441 15.0 1 1 1 1 0 0 NaN 1.0 3 3 SUNDAY 15 0 0 0 0 0 0 Other 0.731105 0.748854 0.508287 0.1546 0.0840 0.9776 0.6940 0.0190 0.0000 0.1034 0.1667 0.2083 0.0149 0.1261 0.0568 0.0000 0.0000 0.1576 0.0871 0.9777 0.7060 0.0191 0.0000 0.1034 0.1667 0.2083 0.0153 0.1377 0.0591 0.0000 0.0000 0.1561 0.0840 0.9776 0.6981 0.0191 0.00 0.1034 0.1667 0.2083 0.0152 0.1283 0.0578 0.0000 0.0000 reg oper account block of flats 0.0550 Stone, brick No 1.0 0.0 1.0 0.0 -2553.0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 1.0 1.0 6.0
165 100194 0 Revolving loans F N Y 2 157500.000 315000.0 15750.0 315000.0 Unaccompanied Working Higher education Civil marriage House / apartment 0.020246 -12805 -5258 -3262.0 -3884 NaN 1 1 0 1 1 0 Laborers 4.0 3 3 WEDNESDAY 11 0 0 0 0 0 0 Industry: type 3 0.434878 0.111767 0.192942 0.0784 0.0806 0.9742 0.6464 0.0080 0.0000 0.1379 0.1667 0.2083 0.0438 0.0614 0.0592 0.0116 0.0124 0.0798 0.0837 0.9742 0.6602 0.0081 0.0000 0.1379 0.1667 0.2083 0.0448 0.0670 0.0617 0.0117 0.0131 0.0791 0.0806 0.9742 0.6511 0.0081 0.00 0.1379 0.1667 0.2083 0.0446 0.0624 0.0603 0.0116 0.0127 reg oper account block of flats 0.0536 Stone, brick No 0.0 0.0 0.0 0.0 -215.0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 1.0 2.0
166 100195 0 Cash loans M Y Y 0 90000.000 336150.0 9373.5 202500.0 Unaccompanied Commercial associate Higher education Single / not married House / apartment 0.030755 -10887 -341 -5333.0 -2828 8.0 1 1 0 1 0 0 NaN 1.0 2 2 FRIDAY 12 0 0 0 0 1 1 Trade: type 3 0.356355 0.547773 0.749022 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 0.0 0.0 0.0 0.0 -114.0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 2.0
167 100196 0 Cash loans F Y Y 0 164250.000 755190.0 32125.5 675000.0 Unaccompanied Working Higher education Married House / apartment 0.018029 -11568 -2331 -1082.0 -824 8.0 1 1 0 1 0 0 Secretaries 2.0 3 3 TUESDAY 7 0 0 0 0 0 0 Construction 0.592547 0.593340 0.303146 NaN NaN 0.9811 NaN NaN NaN 0.1552 0.2500 NaN NaN NaN NaN NaN NaN NaN NaN 0.9786 NaN NaN NaN 0.1034 0.1667 NaN NaN NaN NaN NaN NaN NaN NaN 0.9811 NaN NaN NaN 0.1552 0.2500 NaN NaN NaN NaN NaN NaN NaN block of flats 0.0713 Panel No 0.0 0.0 0.0 0.0 -1606.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 1.0 4.0
168 100197 0 Cash loans F N N 2 157500.000 497520.0 28692.0 450000.0 Unaccompanied Working Secondary / secondary special Married House / apartment 0.003813 -12019 -1148 -682.0 -2588 NaN 1 1 0 1 0 0 Sales staff 4.0 2 2 MONDAY 11 0 0 0 0 0 0 Self-employed 0.721761 0.472286 NaN 0.0763 0.0410 0.9796 NaN NaN 0.0000 0.1379 0.1667 NaN NaN NaN 0.0558 NaN 0.0242 0.0777 0.0425 0.9796 NaN NaN 0.0000 0.1379 0.1667 NaN NaN NaN 0.0581 NaN 0.0256 0.0770 0.0410 0.9796 NaN NaN 0.00 0.1379 0.1667 NaN NaN NaN 0.0568 NaN 0.0247 NaN block of flats 0.0491 Stone, brick No 3.0 0.0 3.0 0.0 -2575.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NaN NaN NaN NaN NaN NaN
169 100198 0 Cash loans M Y Y 1 135000.000 531000.0 32616.0 531000.0 Unaccompanied Working Secondary / secondary special Married House / apartment 0.028663 -13116 -1786 -7237.0 -3168 1.0 1 1 0 1 0 0 Drivers 3.0 2 2 MONDAY 10 0 0 0 0 0 0 Self-employed NaN 0.748916 0.639708 0.0722 0.0020 0.9781 0.7008 0.0078 0.0000 0.1379 0.1667 0.0000 0.0701 0.0588 0.0677 0.0000 0.0016 0.0735 0.0020 0.9782 0.7125 0.0079 0.0000 0.1379 0.1667 0.0000 0.0717 0.0643 0.0706 0.0000 0.0017 0.0729 0.0020 0.9781 0.7048 0.0079 0.00 0.1379 0.1667 0.0000 0.0713 0.0599 0.0689 0.0000 0.0016 reg oper account block of flats 0.0579 Stone, brick No 0.0 0.0 0.0 0.0 0.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 2.0
170 100199 0 Cash loans F N Y 0 112500.000 269550.0 18891.0 225000.0 Unaccompanied Working Secondary / secondary special Married House / apartment 0.022625 -20858 -472 -8167.0 -4411 NaN 1 1 0 1 0 0 Cleaning staff 2.0 2 2 WEDNESDAY 15 0 0 0 0 1 1 Industry: type 3 NaN 0.704424 0.276441 0.0825 0.0756 0.9896 0.8572 0.0140 0.0000 0.0345 0.1667 0.2083 0.0131 0.0672 0.0490 0.0000 0.0000 0.0840 0.0785 0.9896 0.8628 0.0141 0.0000 0.0345 0.1667 0.2083 0.0134 0.0735 0.0511 0.0000 0.0000 0.0833 0.0756 0.9896 0.8591 0.0141 0.00 0.0345 0.1667 0.2083 0.0134 0.0684 0.0499 0.0000 0.0000 reg oper account block of flats 0.0462 Stone, brick No 0.0 0.0 0.0 0.0 -738.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 1.0
171 100200 0 Cash loans F N N 0 216000.000 450000.0 27193.5 450000.0 Unaccompanied Working Secondary / secondary special Widow House / apartment 0.026392 -16891 -2730 -7519.0 -434 NaN 1 1 1 1 1 0 NaN 1.0 2 2 MONDAY 10 0 0 0 0 0 0 Business Entity Type 3 NaN 0.640041 0.694093 0.1959 0.1741 0.9841 NaN NaN 0.0000 0.4483 0.1667 NaN NaN NaN 0.1762 NaN NaN 0.1996 0.1807 0.9841 NaN NaN 0.0000 0.4483 0.1667 NaN NaN NaN 0.1835 NaN NaN 0.1978 0.1741 0.9841 NaN NaN 0.00 0.4483 0.1667 NaN NaN NaN 0.1793 NaN NaN NaN block of flats 0.1795 Panel No 4.0 0.0 3.0 0.0 -332.0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 3.0
172 100201 0 Cash loans F N Y 1 180000.000 675000.0 36747.0 675000.0 Unaccompanied Commercial associate Higher education Single / not married House / apartment 0.035792 -12147 -2178 -6189.0 -1719 NaN 1 1 0 1 0 0 Core staff 2.0 2 2 SATURDAY 10 0 0 0 0 0 0 Trade: type 2 0.764917 0.601560 0.350696 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 10.0 0.0 10.0 0.0 -1754.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 3.0 6.0
173 100202 0 Cash loans F N N 0 67500.000 127350.0 6597.0 112500.0 Unaccompanied Pensioner Secondary / secondary special Single / not married House / apartment 0.019101 -22994 365243 -9705.0 -4078 NaN 1 0 0 1 0 0 NaN 1.0 2 2 TUESDAY 12 0 0 0 0 0 0 XNA NaN 0.121906 0.269286 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 2.0 0.0 2.0 0.0 -632.0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 1.0 5.0
174 100203 0 Cash loans F N N 0 54000.000 119925.0 11097.0 112500.0 Other_B Pensioner Secondary / secondary special Widow House / apartment 0.010276 -25104 365243 -4182.0 -4210 NaN 1 0 0 1 0 0 NaN 1.0 2 2 TUESDAY 9 0 0 0 0 0 0 XNA NaN 0.640514 0.544235 NaN NaN NaN NaN NaN NaN NaN 0.0000 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 0.0000 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 0.0000 NaN NaN NaN NaN NaN NaN NaN NaN 0.0026 Wooden No 0.0 0.0 0.0 0.0 -2.0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 0.0
175 100204 0 Cash loans F N Y 1 81000.000 532494.0 30699.0 454500.0 Unaccompanied Pensioner Secondary / secondary special Married House / apartment 0.009334 -19525 365243 -5077.0 -1589 NaN 1 0 0 1 1 0 NaN 3.0 2 2 WEDNESDAY 9 0 0 0 0 0 0 XNA NaN 0.171535 0.540654 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 0.0 0.0 0.0 0.0 -7.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 2.0 0.0
176 100205 0 Cash loans F N Y 0 63000.000 808650.0 26217.0 675000.0 Unaccompanied Working Secondary / secondary special Married House / apartment 0.018209 -18302 -1373 -10539.0 -1210 NaN 1 1 0 1 0 0 Laborers 2.0 3 3 TUESDAY 11 0 0 0 0 0 0 Housing NaN 0.046216 0.549597 0.1392 0.1753 0.9856 0.8028 0.2698 0.0000 0.3448 0.1667 0.0417 0.0957 0.1135 0.1203 NaN 0.0410 0.1418 0.1819 0.9856 0.8105 0.2723 0.0000 0.3448 0.1667 0.0417 0.0979 0.1240 0.1253 NaN 0.0434 0.1405 0.1753 0.9856 0.8054 0.2715 0.00 0.3448 0.1667 0.0417 0.0974 0.1154 0.1224 NaN 0.0419 reg oper account block of flats 0.1475 Stone, brick No 0.0 0.0 0.0 0.0 -1111.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 0.0
177 100206 0 Cash loans M Y N 1 157500.000 318528.0 25294.5 252000.0 Unaccompanied Commercial associate Secondary / secondary special Married House / apartment 0.022800 -12027 -1720 -3867.0 -4420 12.0 1 1 0 1 1 1 Drivers 3.0 2 2 FRIDAY 8 0 0 0 0 1 1 Business Entity Type 3 NaN 0.164259 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 0.0 0.0 0.0 0.0 0.0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 NaN NaN NaN NaN NaN NaN
178 100207 0 Cash loans F N Y 0 157500.000 700830.0 22738.5 585000.0 Spouse, partner Working Secondary / secondary special Civil marriage House / apartment 0.018850 -16518 -3592 -3627.0 -44 NaN 1 1 0 1 0 0 Waiters/barmen staff 2.0 2 2 SATURDAY 10 0 0 0 0 0 0 Government NaN 0.671783 0.593718 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 0.0 0.0 0.0 0.0 -2709.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 1.0
179 100208 0 Cash loans M N N 1 180000.000 888840.0 29016.0 675000.0 Family Commercial associate Secondary / secondary special Civil marriage House / apartment 0.007114 -12648 -2802 -2109.0 -4594 NaN 1 1 0 1 0 0 Laborers 3.0 2 2 FRIDAY 11 0 0 0 0 0 0 Self-employed 0.342712 0.634455 0.706205 0.0351 0.0547 0.9886 0.8436 0.0099 0.0000 0.1034 0.1667 0.2083 NaN 0.0286 0.0451 0.0039 0.0062 0.0357 0.0568 0.9886 0.8497 0.0100 0.0000 0.1034 0.1667 0.2083 NaN 0.0312 0.0470 0.0039 0.0066 0.0354 0.0547 0.9886 0.8457 0.0100 0.00 0.1034 0.1667 0.2083 NaN 0.0291 0.0459 0.0039 0.0063 reg oper account block of flats 0.0409 Mixed No 0.0 0.0 0.0 0.0 -1127.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 3.0 2.0
180 100209 1 Revolving loans M N Y 3 180000.000 540000.0 27000.0 540000.0 Unaccompanied Commercial associate Higher education Married House / apartment 0.024610 -15326 -1038 -782.0 -792 NaN 1 1 0 1 0 0 High skill tech staff 5.0 2 2 THURSDAY 18 0 0 0 0 0 0 Construction NaN 0.372110 0.506484 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 0.0 0.0 0.0 0.0 -429.0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 2.0
181 100210 0 Cash loans F N Y 1 202500.000 771493.5 25627.5 666000.0 Unaccompanied Working Higher education Separated House / apartment 0.005313 -16475 -17 -1225.0 -25 NaN 1 1 0 1 0 0 Core staff 2.0 2 2 MONDAY 12 0 0 0 0 0 0 School NaN 0.621978 0.744932 0.1763 0.0933 0.9980 NaN NaN 0.1200 0.1034 0.3333 NaN 0.0640 NaN 0.0866 NaN 0.0000 0.1796 0.0968 0.9980 NaN NaN 0.1208 0.1034 0.3333 NaN 0.0655 NaN 0.0903 NaN 0.0000 0.1780 0.0933 0.9980 NaN NaN 0.12 0.1034 0.3333 NaN 0.0651 NaN 0.0882 NaN 0.0000 NaN block of flats 0.1562 Panel No 0.0 0.0 0.0 0.0 -1488.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 1.0
182 100211 0 Cash loans M N N 3 225000.000 746280.0 49873.5 675000.0 Unaccompanied Commercial associate Secondary / secondary special Married House / apartment 0.006233 -14033 -328 -544.0 -554 NaN 1 1 1 1 0 0 Cooking staff 5.0 2 2 TUESDAY 14 0 0 0 1 1 0 Other NaN 0.575086 0.307737 0.0742 NaN 0.9846 NaN NaN 0.0800 0.0690 0.3333 NaN NaN NaN 0.0757 NaN 0.0169 0.0756 NaN 0.9846 NaN NaN 0.0806 0.0690 0.3333 NaN NaN NaN 0.0789 NaN 0.0178 0.0749 NaN 0.9846 NaN NaN 0.08 0.0690 0.3333 NaN NaN NaN 0.0771 NaN 0.0172 NaN block of flats 0.0640 Panel No 0.0 0.0 0.0 0.0 -534.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 6.0
183 100213 0 Cash loans F N N 2 49500.000 278460.0 19930.5 225000.0 Unaccompanied Working Secondary / secondary special Married House / apartment 0.031329 -13873 -1282 -632.0 -5042 NaN 1 1 0 1 0 1 Laborers 4.0 2 2 SUNDAY 10 0 0 0 0 0 0 Industry: type 3 0.549717 0.495366 0.581484 0.0619 0.0530 0.9771 0.6872 0.0078 0.0000 0.1379 0.1667 0.2083 0.0115 NaN 0.0540 NaN 0.0000 0.0630 0.0550 0.9772 0.6994 0.0079 0.0000 0.1379 0.1667 0.2083 0.0118 NaN 0.0563 NaN 0.0000 0.0625 0.0530 0.9771 0.6914 0.0079 0.00 0.1379 0.1667 0.2083 0.0117 NaN 0.0550 NaN 0.0000 reg oper account block of flats 0.0425 Panel No 0.0 0.0 0.0 0.0 -2690.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 1.0 0.0 0.0
184 100214 1 Cash loans F N Y 1 202500.000 436032.0 28516.5 360000.0 Unaccompanied Commercial associate Higher education Civil marriage House / apartment 0.032561 -10127 -111 -1724.0 -337 NaN 1 1 0 1 1 0 NaN 3.0 1 1 WEDNESDAY 14 0 0 0 0 0 0 Business Entity Type 3 NaN 0.500231 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN No 0.0 0.0 0.0 0.0 -130.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NaN NaN NaN NaN NaN NaN
185 100215 0 Cash loans F Y N 0 90000.000 227520.0 8172.0 180000.0 Unaccompanied Pensioner Secondary / secondary special Civil marriage House / apartment 0.025164 -21703 365243 -9849.0 -4980 11.0 1 0 0 1 1 0 NaN 2.0 2 2 THURSDAY 13 0 0 0 0 0 0 XNA NaN 0.265312 0.641368 0.0330 0.0146 0.9692 0.5784 0.0408 0.0000 0.1034 0.1250 0.1667 0.0145 0.0210 0.0380 0.0270 0.0296 0.0336 0.0152 0.9692 0.5949 0.0412 0.0000 0.1034 0.1250 0.1667 0.0149 0.0230 0.0396 0.0272 0.0313 0.0333 0.0146 0.9692 0.5840 0.0411 0.00 0.1034 0.1250 0.1667 0.0148 0.0214 0.0387 0.0272 0.0302 reg oper account block of flats 0.0586 Mixed No 0.0 0.0 0.0 0.0 -2028.0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 5.0 0.0 5.0
186 100216 0 Cash loans F N N 0 90000.000 67500.0 6705.0 67500.0 Unaccompanied Working Secondary / secondary special Married Municipal apartment 0.018209 -19785 -9919 -9342.0 -3310 NaN 1 1 1 1 1 0 Sales staff 2.0 3 3 TUESDAY 13 0 0 0 0 0 0 Business Entity Type 3 0.620020 0.709453 NaN 0.1969 0.1430 0.9876 NaN NaN 0.2000 0.1724 0.3333 NaN 0.0510 NaN 0.1281 NaN 0.0564 0.2006 0.1484 0.9876 NaN NaN 0.2014 0.1724 0.3333 NaN 0.0521 NaN 0.1334 NaN 0.0598 0.1988 0.1430 0.9876 NaN NaN 0.20 0.1724 0.3333 NaN 0.0518 NaN 0.1304 NaN 0.0576 NaN block of flats 0.1658 Panel No 1.0 0.0 1.0 0.0 -1.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 0.0
187 100217 0 Cash loans F N N 0 180000.000 254700.0 25321.5 225000.0 Family Working Secondary / secondary special Separated House / apartment 0.004960 -13620 -1403 -5451.0 -4225 NaN 1 1 0 1 0 1 Laborers 1.0 2 2 MONDAY 14 0 0 0 0 0 0 Self-employed 0.521633 0.391436 0.427657 0.0082 NaN 0.9518 NaN NaN NaN NaN 0.0417 NaN NaN NaN NaN NaN NaN 0.0084 NaN 0.9518 NaN NaN NaN NaN 0.0417 NaN NaN NaN NaN NaN NaN 0.0083 NaN 0.9518 NaN NaN NaN NaN 0.0417 NaN NaN NaN NaN NaN NaN NaN block of flats 0.0015 NaN No 1.0 0.0 1.0 0.0 -252.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 2.0 1.0
188 100218 0 Cash loans M Y Y 0 337500.000 876078.0 49050.0 765000.0 Unaccompanied Commercial associate Higher education Single / not married House / apartment 0.072508 -13210 -225 -7318.0 -4397 3.0 1 1 0 1 0 0 Drivers 1.0 1 1 SATURDAY 12 0 0 0 0 0 0 Business Entity Type 3 0.667167 0.749409 0.337673 NaN NaN 0.9876 NaN NaN NaN NaN NaN NaN NaN NaN 0.2256 NaN 0.0063 NaN NaN 0.9876 NaN NaN NaN NaN NaN NaN NaN NaN 0.2350 NaN 0.0067 NaN NaN 0.9876 NaN NaN NaN NaN NaN NaN NaN NaN 0.2296 NaN 0.0065 NaN block of flats 0.2152 NaN No 0.0 0.0 0.0 0.0 -382.0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 2.0
189 100219 0 Cash loans M N Y 1 315000.000 2250000.0 83515.5 2250000.0 Unaccompanied Commercial associate Higher education Married House / apartment 0.032561 -11177 -1514 -64.0 -2793 NaN 1 1 0 1 0 1 Core staff 3.0 1 1 FRIDAY 16 0 0 0 0 0 0 Business Entity Type 3 0.380092 0.693358 NaN 0.4753 0.0694 0.9950 NaN NaN 0.5200 0.1724 0.9583 NaN 0.1213 NaN 0.6651 NaN 0.3558 0.4842 0.0720 0.9950 NaN NaN 0.5236 0.1724 0.9583 NaN 0.1241 NaN 0.6930 NaN 0.3767 0.4799 0.0694 0.9950 NaN NaN 0.52 0.1724 0.9583 NaN 0.1234 NaN 0.6771 NaN 0.3633 NaN block of flats 0.6216 Monolithic No 0.0 0.0 0.0 0.0 -926.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 3.0
190 100220 0 Cash loans F N Y 0 225000.000 1040985.0 30568.5 909000.0 Unaccompanied Commercial associate Higher education Single / not married House / apartment 0.003818 -14823 -185 -1147.0 -4138 NaN 1 1 0 1 0 0 Sales staff 1.0 2 2 TUESDAY 6 0 0 0 0 0 0 Self-employed NaN 0.626339 0.607557 0.0907 0.1012 0.9816 0.7484 NaN 0.0000 0.2069 0.1667 0.2083 NaN 0.0740 0.0854 0.0000 0.0390 0.0924 0.1050 0.9816 0.7583 NaN 0.0000 0.2069 0.1667 0.2083 NaN 0.0808 0.0889 0.0000 0.0413 0.0916 0.1012 0.9816 0.7518 NaN 0.00 0.2069 0.1667 0.2083 NaN 0.0752 0.0869 0.0000 0.0398 reg oper account block of flats 0.0681 Panel No 2.0 0.0 2.0 0.0 -1898.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 2.0 1.0
191 100221 0 Cash loans F N Y 0 225000.000 684054.0 77494.5 630000.0 Unaccompanied Commercial associate Secondary / secondary special Widow House / apartment 0.006629 -23656 -11883 -7125.0 -4681 NaN 1 1 0 1 1 0 NaN 1.0 2 2 TUESDAY 9 0 0 0 0 0 0 Bank NaN 0.592459 0.736623 0.2598 0.1158 0.9791 0.7144 NaN 0.0000 0.2069 0.1667 0.2083 0.0218 0.1765 0.0935 0.1622 0.0319 0.2647 0.1202 0.9791 0.7256 NaN 0.0000 0.2069 0.1667 0.2083 0.0223 0.1928 0.0974 0.1634 0.0338 0.2623 0.1158 0.9791 0.7182 NaN 0.00 0.2069 0.1667 0.2083 0.0222 0.1796 0.0952 0.1630 0.0326 NaN block of flats 0.0805 Block No 0.0 0.0 0.0 0.0 -584.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 1.0 4.0
192 100224 0 Cash loans F Y Y 2 225000.000 1256400.0 36864.0 900000.0 Unaccompanied Commercial associate Secondary / secondary special Married House / apartment 0.019689 -14091 -5664 -8127.0 -2462 7.0 1 1 0 1 0 0 NaN 4.0 2 2 TUESDAY 9 0 0 0 0 0 0 Business Entity Type 3 NaN 0.598632 0.344155 0.0742 0.0564 0.9826 0.7620 0.0044 0.0800 0.0690 0.3333 0.0417 0.0403 0.0605 0.0762 0.0000 0.0000 0.0756 0.0585 0.9826 0.7713 0.0044 0.0806 0.0690 0.3333 0.0417 0.0412 0.0661 0.0794 0.0000 0.0000 0.0749 0.0564 0.9826 0.7652 0.0044 0.08 0.0690 0.3333 0.0417 0.0410 0.0616 0.0776 0.0000 0.0000 org spec account block of flats 0.0726 Panel No 4.0 2.0 4.0 2.0 0.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 0.0
193 100225 0 Cash loans M Y N 2 157500.000 720000.0 28552.5 720000.0 Unaccompanied Commercial associate Secondary / secondary special Married With parents 0.030755 -14144 -4516 -1516.0 -4616 7.0 1 1 0 1 1 0 Sales staff 4.0 2 2 THURSDAY 10 0 0 0 0 1 1 Self-employed NaN 0.722178 0.634706 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 0.0 0.0 0.0 0.0 -1450.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 2.0
194 100226 0 Revolving loans F N N 2 112500.000 270000.0 13500.0 270000.0 Unaccompanied Working Secondary / secondary special Married House / apartment 0.009657 -13584 -680 -350.0 -2097 NaN 1 1 0 1 0 0 Sales staff 4.0 2 2 TUESDAY 16 0 0 0 0 0 0 Self-employed 0.394163 0.636572 NaN 0.1148 0.0840 0.9945 0.9252 0.0034 0.0932 0.1262 0.3054 0.3471 0.0393 0.0936 0.1036 0.0000 0.0000 0.0630 0.0955 0.9975 0.9673 0.0000 0.0000 0.1379 0.3750 0.4167 0.0151 0.0551 0.0637 0.0000 0.0000 0.1218 0.0921 0.9975 0.9665 0.0000 0.12 0.1379 0.3750 0.4167 0.0206 0.1000 0.1027 0.0000 0.0000 reg oper account block of flats 0.0544 Panel No 0.0 0.0 0.0 0.0 0.0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NaN NaN NaN NaN NaN NaN
195 100227 0 Cash loans M N N 0 292500.000 1024740.0 55719.0 900000.0 Unaccompanied Working Higher education Single / not married House / apartment 0.006207 -10010 -240 -4629.0 -2680 NaN 1 1 1 1 1 0 Managers 1.0 2 2 WEDNESDAY 14 0 0 0 1 1 0 Self-employed 0.412944 0.634896 0.297087 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 0.0 0.0 0.0 0.0 -964.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 1.0 1.0
196 100228 0 Cash loans F N N 1 121500.000 1102500.0 32364.0 1102500.0 Unaccompanied State servant Secondary / secondary special Married Municipal apartment 0.009657 -13957 -410 -5038.0 -4630 NaN 1 1 1 1 1 0 Laborers 3.0 2 2 MONDAY 13 0 0 0 0 0 0 Business Entity Type 2 0.741096 0.408871 0.425893 0.0649 0.0935 0.9806 0.7348 NaN 0.0000 0.1379 0.1667 0.2083 0.0805 0.0471 0.0542 0.0270 0.0983 0.0662 0.0970 0.9806 0.7452 NaN 0.0000 0.1379 0.1667 0.2083 0.0824 0.0514 0.0565 0.0272 0.1040 0.0656 0.0935 0.9806 0.7383 NaN 0.00 0.1379 0.1667 0.2083 0.0819 0.0479 0.0552 0.0272 0.1003 NaN block of flats 0.0477 Stone, brick No 0.0 0.0 0.0 0.0 -317.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 1.0 0.0 4.0
197 100229 0 Cash loans F N Y 0 157500.000 544491.0 16047.0 454500.0 Family Working Secondary / secondary special Married House / apartment 0.010147 -17947 -5025 -11812.0 -1505 NaN 1 1 0 1 0 0 Medicine staff 2.0 2 2 TUESDAY 17 0 0 0 0 0 0 Medicine NaN 0.641012 0.759712 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 0.0 0.0 0.0 0.0 -122.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 2.0
198 100230 0 Revolving loans F N N 0 135000.000 405000.0 20250.0 405000.0 Children Working Secondary / secondary special Married House / apartment 0.008474 -19070 -837 -6789.0 -2595 NaN 1 1 1 1 0 0 Sales staff 2.0 2 2 WEDNESDAY 11 0 0 0 0 0 0 Self-employed 0.621555 0.371543 0.180888 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 10.0 1.0 10.0 0.0 -481.0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 2.0
199 100231 0 Cash loans M Y Y 1 108000.000 912240.0 30276.0 787500.0 Spouse, partner Commercial associate Secondary / secondary special Married House / apartment 0.018634 -17850 -2899 -2880.0 -1400 9.0 1 1 0 1 0 0 Drivers 3.0 2 2 THURSDAY 9 0 0 0 0 1 1 Industry: type 3 NaN 0.497301 0.832785 0.0701 0.1336 0.9816 NaN NaN 0.0000 0.1207 0.1250 NaN 0.0510 0.1009 0.0736 0.0000 0.0011 0.0168 0.1387 0.9737 NaN NaN 0.0000 0.0690 0.0417 NaN 0.0522 0.1102 0.0151 0.0000 0.0012 0.0708 0.1336 0.9816 NaN NaN 0.00 0.1207 0.1250 NaN 0.0519 0.1026 0.0749 0.0000 0.0011 NaN block of flats 0.0114 Stone, brick No 1.0 0.0 1.0 0.0 0.0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 1.0 0.0 0.0 0.0 2.0
200 100233 0 Cash loans F N Y 0 216000.000 679671.0 28926.0 607500.0 Unaccompanied Working Secondary / secondary special Married House / apartment 0.010966 -18341 -701 -9730.0 -1876 NaN 1 1 0 1 0 0 NaN 2.0 2 2 WEDNESDAY 10 0 0 0 0 0 0 Transport: type 4 NaN 0.201584 0.609276 0.1031 0.0838 0.9826 0.7620 0.0090 0.0000 0.2069 0.1667 0.2083 0.0000 0.0841 0.0943 0.0000 0.0000 0.1050 0.0870 0.9826 0.7713 0.0091 0.0000 0.2069 0.1667 0.2083 0.0000 0.0918 0.0982 0.0000 0.0000 0.1041 0.0838 0.9826 0.7652 0.0090 0.00 0.2069 0.1667 0.2083 0.0000 0.0855 0.0960 0.0000 0.0000 reg oper account block of flats 0.0791 Panel No 1.0 1.0 1.0 0.0 -2071.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 8.0
201 100234 0 Revolving loans F N N 0 67500.000 180000.0 9000.0 180000.0 Unaccompanied Working Secondary / secondary special Widow With parents 0.003069 -16614 -551 -6098.0 -166 NaN 1 1 0 1 0 0 Core staff 1.0 3 3 THURSDAY 12 0 0 0 0 0 0 Hotel NaN 0.583926 0.716570 0.0649 0.0787 0.9752 NaN NaN 0.0000 0.1379 0.1667 NaN 0.0405 NaN 0.0337 NaN 0.0333 0.0662 0.0816 0.9752 NaN NaN 0.0000 0.1379 0.1667 NaN 0.0414 NaN 0.0352 NaN 0.0353 0.0656 0.0787 0.9752 NaN NaN 0.00 0.1379 0.1667 NaN 0.0412 NaN 0.0344 NaN 0.0340 NaN block of flats 0.0550 Stone, brick No 0.0 0.0 0.0 0.0 0.0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 1.0 0.0
202 100235 0 Cash loans F N Y 0 135000.000 364896.0 19926.0 315000.0 Unaccompanied Working Secondary / secondary special Married House / apartment 0.016612 -15887 -468 -4604.0 -3739 NaN 1 1 0 1 0 0 Cooking staff 2.0 2 2 WEDNESDAY 12 0 0 0 1 0 1 Military NaN 0.505288 0.218859 0.0660 0.0891 0.9762 0.6736 0.0079 0.0000 0.1379 0.1250 0.1667 0.0120 0.0538 0.0499 0.0000 0.0000 0.0672 0.0925 0.9762 0.6864 0.0080 0.0000 0.1379 0.1250 0.1667 0.0123 0.0588 0.0520 0.0000 0.0000 0.0666 0.0891 0.9762 0.6780 0.0079 0.00 0.1379 0.1250 0.1667 0.0123 0.0547 0.0508 0.0000 0.0000 not specified block of flats 0.0436 Stone, brick No 1.0 0.0 1.0 0.0 -736.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 2.0
203 100236 0 Cash loans M Y Y 0 112500.000 312768.0 20353.5 270000.0 Unaccompanied Working Secondary / secondary special Married House / apartment 0.028663 -23187 -1896 -5213.0 -4265 21.0 1 1 0 1 0 0 Laborers 2.0 2 2 TUESDAY 16 0 0 0 0 1 1 Business Entity Type 2 NaN 0.430618 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 2.0 0.0 2.0 0.0 -149.0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 NaN NaN NaN NaN NaN NaN
204 100237 0 Cash loans F N N 2 144000.000 1288350.0 37800.0 1125000.0 Unaccompanied Working Secondary / secondary special Civil marriage Rented apartment 0.010966 -13803 -281 -640.0 -3215 NaN 1 1 1 1 1 0 Core staff 4.0 2 2 SATURDAY 11 1 1 0 1 1 0 Trade: type 3 NaN 0.547036 0.217629 0.0186 NaN 0.9995 0.9932 NaN 0.0000 0.0690 0.0833 NaN 0.0067 0.0151 0.0192 NaN 0.0076 0.0189 NaN 0.9995 0.9935 NaN 0.0000 0.0690 0.0833 NaN 0.0068 0.0165 0.0200 NaN 0.0080 0.0187 NaN 0.9995 0.9933 NaN 0.00 0.0690 0.0833 NaN 0.0068 0.0154 0.0195 NaN 0.0077 reg oper account block of flats 0.0167 Panel No 1.0 0.0 1.0 0.0 -533.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 1.0
205 100238 0 Cash loans M Y Y 2 135000.000 781920.0 50148.0 675000.0 Spouse, partner Commercial associate Secondary / secondary special Married House / apartment 0.035792 -12994 -1330 -2588.0 -1202 21.0 1 1 0 1 0 0 Laborers 4.0 2 2 SUNDAY 12 0 0 0 0 1 1 Business Entity Type 3 0.437289 0.645634 0.654529 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 0.0 0.0 0.0 0.0 -334.0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 0.0
206 100239 0 Cash loans F N Y 1 225000.000 1483231.5 51687.0 1354500.0 Unaccompanied Working Secondary / secondary special Married House / apartment 0.010147 -20191 -1699 -6040.0 -3723 NaN 1 1 0 1 0 1 Accountants 3.0 2 2 MONDAY 15 0 0 0 0 0 0 Business Entity Type 3 0.853080 0.757328 0.130947 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 2.0 1.0 2.0 0.0 -1864.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 1.0 2.0
207 100240 0 Cash loans M N Y 2 135000.000 590337.0 28530.0 477000.0 Unaccompanied Working Secondary / secondary special Married House / apartment 0.018029 -11978 -3947 -6005.0 -4099 NaN 1 1 0 1 1 0 Laborers 4.0 3 2 SUNDAY 8 0 0 0 0 0 0 Housing 0.244903 0.628532 0.121408 0.0830 0.0560 0.9816 0.8232 0.0681 0.1000 0.1034 0.1875 0.4167 0.1387 0.1278 0.0967 0.0309 0.0158 0.0011 0.0000 0.9762 0.8301 0.0687 0.0000 0.0345 0.0000 0.4167 0.1419 0.1396 0.0009 0.0311 0.0000 0.0838 0.0560 0.9816 0.8256 0.0685 0.10 0.1034 0.1875 0.4167 0.1411 0.1300 0.0984 0.0311 0.0162 reg oper account terraced house 0.1955 Wooden No 4.0 1.0 4.0 1.0 -155.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 4.0
208 100242 0 Cash loans M Y Y 0 90000.000 101880.0 10827.0 90000.0 Unaccompanied Working Secondary / secondary special Married Municipal apartment 0.005144 -20945 -7359 -7395.0 -4287 11.0 1 1 1 1 0 0 Drivers 2.0 2 2 SATURDAY 12 0 0 0 0 0 0 Business Entity Type 3 NaN 0.718314 0.380800 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 0.0 0.0 0.0 0.0 -2278.0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 0.0
209 100243 0 Cash loans M N Y 0 112500.000 161730.0 13833.0 135000.0 Unaccompanied Working Higher education Married House / apartment 0.028663 -9720 -573 -689.0 -2193 NaN 1 1 1 1 1 0 Laborers 2.0 2 2 SUNDAY 13 0 0 0 0 1 1 Business Entity Type 3 NaN 0.394075 0.454321 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 3.0 0.0 3.0 0.0 -1197.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 3.0
210 100244 0 Cash loans M N N 1 247500.000 447768.0 35505.0 405000.0 Unaccompanied Working Secondary / secondary special Married House / apartment 0.010006 -17103 -5021 -221.0 -647 NaN 1 1 1 1 0 0 Laborers 3.0 2 2 SATURDAY 14 0 0 0 0 0 0 Industry: type 9 0.405098 0.103570 0.742182 0.0866 0.1252 0.9876 NaN NaN 0.0000 0.2069 0.1667 NaN 0.0551 NaN 0.0905 NaN 0.0428 0.0882 0.1299 0.9876 NaN NaN 0.0000 0.2069 0.1667 NaN 0.0563 NaN 0.0943 NaN 0.0453 0.0874 0.1252 0.9876 NaN NaN 0.00 0.2069 0.1667 NaN 0.0560 NaN 0.0921 NaN 0.0437 NaN block of flats 0.0900 Panel No 0.0 0.0 0.0 0.0 0.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 0.0
211 100246 1 Cash loans F N Y 0 135000.000 495216.0 26995.5 427500.0 Unaccompanied Working Secondary / secondary special Married House / apartment 0.008019 -12824 -154 -6764.0 -126 NaN 1 1 0 1 0 0 Laborers 2.0 2 2 THURSDAY 10 0 0 0 0 0 0 Self-employed NaN 0.044021 0.243186 0.0577 0.0854 0.9826 0.7620 0.0150 0.0000 0.1379 0.1667 0.2083 0.0530 0.0454 0.0544 0.0077 0.0487 0.0588 0.0886 0.9826 0.7713 0.0151 0.0000 0.1379 0.1667 0.2083 0.0542 0.0496 0.0567 0.0078 0.0515 0.0583 0.0854 0.9826 0.7652 0.0151 0.00 0.1379 0.1667 0.2083 0.0539 0.0462 0.0554 0.0078 0.0497 reg oper account block of flats 0.0616 Stone, brick No 0.0 0.0 0.0 0.0 -309.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 0.0
212 100247 0 Cash loans F N Y 0 225000.000 402939.0 19381.5 306000.0 Unaccompanied Working Secondary / secondary special Married House / apartment 0.010147 -19914 -3408 -7647.0 -3444 NaN 1 1 0 1 1 0 Laborers 2.0 2 2 FRIDAY 11 0 1 1 0 1 1 Business Entity Type 1 0.582492 0.657784 0.196334 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 2.0 1.0 2.0 0.0 -1498.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 4.0
213 100248 0 Cash loans F N Y 0 90000.000 808650.0 23643.0 675000.0 Unaccompanied Pensioner Secondary / secondary special Civil marriage House / apartment 0.007305 -22389 365243 -12314.0 -4884 NaN 1 0 0 1 0 0 NaN 2.0 3 3 WEDNESDAY 12 0 0 0 0 0 0 XNA NaN 0.223495 0.456110 0.0082 NaN 0.9414 NaN NaN NaN NaN 0.0417 NaN NaN NaN 0.0070 NaN NaN 0.0084 NaN 0.9414 NaN NaN NaN NaN 0.0417 NaN NaN NaN 0.0055 NaN NaN 0.0083 NaN 0.9414 NaN NaN NaN NaN 0.0417 NaN NaN NaN 0.0071 NaN NaN NaN block of flats 0.0042 Wooden No 1.0 0.0 1.0 0.0 -1386.0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 3.0
214 100249 0 Cash loans F N Y 2 67500.000 76410.0 4513.5 67500.0 Unaccompanied Working Secondary / secondary special Married House / apartment 0.010500 -15293 -5317 -4699.0 -4753 NaN 1 1 1 1 0 0 Laborers 4.0 3 3 FRIDAY 15 0 0 0 0 1 1 Industry: type 3 0.636498 0.093445 0.519097 0.0340 NaN 0.9816 NaN NaN 0.0000 NaN 0.0833 NaN NaN NaN 0.0269 NaN 0.0000 0.0347 NaN 0.9816 NaN NaN 0.0000 NaN 0.0833 NaN NaN NaN 0.0281 NaN 0.0000 0.0344 NaN 0.9816 NaN NaN 0.00 NaN 0.0833 NaN NaN NaN 0.0274 NaN 0.0000 NaN NaN 0.0212 Stone, brick No 0.0 0.0 0.0 0.0 -1655.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 1.0 0.0
215 100250 0 Cash loans F Y N 1 72000.000 90000.0 6529.5 90000.0 Unaccompanied Working Secondary / secondary special Married With parents 0.010966 -11259 -164 -808.0 -3951 17.0 1 1 0 1 0 0 Laborers 3.0 2 2 FRIDAY 12 0 0 0 0 0 0 Business Entity Type 3 NaN 0.295433 0.621226 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 0.0 0.0 0.0 0.0 -1203.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 1.0 5.0
216 100251 0 Cash loans M Y Y 0 202500.000 270000.0 14134.5 270000.0 Unaccompanied Working Secondary / secondary special Married House / apartment 0.025164 -19530 -1940 -7092.0 -3060 12.0 1 1 0 1 1 0 Drivers 2.0 2 2 THURSDAY 8 0 0 0 0 1 1 Other 0.690437 0.581022 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 0.0 0.0 0.0 0.0 -1424.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 0.0
217 100252 0 Cash loans F N Y 0 225000.000 490495.5 50391.0 454500.0 Unaccompanied Working Secondary / secondary special Married House / apartment 0.006629 -17715 -1256 -5284.0 -1238 NaN 1 1 0 1 0 0 Sales staff 2.0 2 2 WEDNESDAY 10 0 0 0 0 0 0 Self-employed NaN 0.670967 NaN 0.2320 0.2113 0.9886 0.8436 0.0640 0.2000 0.1724 0.3333 0.0417 0.1420 0.1858 0.2913 0.0154 0.0199 0.2363 0.2192 0.9886 0.8497 0.0646 0.2014 0.1724 0.3333 0.0417 0.1452 0.2029 0.3035 0.0156 0.0210 0.2342 0.2113 0.9886 0.8457 0.0644 0.20 0.1724 0.3333 0.0417 0.1445 0.1890 0.2966 0.0155 0.0203 org spec account block of flats 0.2685 Panel No 3.0 0.0 3.0 0.0 -470.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 1.0 1.0
218 100254 0 Cash loans M Y Y 0 292500.000 1078200.0 34911.0 900000.0 Unaccompanied Commercial associate Higher education Married House / apartment 0.046220 -21646 -2127 -381.0 -4264 2.0 1 1 0 1 1 0 Drivers 2.0 1 1 FRIDAY 15 0 0 0 0 0 0 Business Entity Type 3 0.717761 0.750443 0.511892 0.0722 0.0575 NaN 0.8708 0.0137 NaN 0.0345 0.1667 0.0417 NaN NaN NaN NaN NaN 0.0735 0.0597 NaN 0.8759 0.0138 NaN 0.0345 0.1667 0.0417 NaN NaN NaN NaN NaN 0.0729 0.0575 NaN 0.8725 0.0138 NaN 0.0345 0.1667 0.0417 NaN NaN NaN NaN NaN reg oper spec account NaN 0.0376 Stone, brick No 2.0 1.0 2.0 1.0 -307.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 3.0
219 100255 0 Cash loans M Y Y 0 315000.000 824544.0 52825.5 720000.0 Unaccompanied Working Secondary / secondary special Single / not married House / apartment 0.046220 -15377 -4398 -34.0 -5046 3.0 1 1 0 1 0 0 Laborers 1.0 1 1 MONDAY 15 0 0 0 0 0 0 Business Entity Type 3 0.593710 0.611886 0.420611 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 0.0 0.0 0.0 0.0 -567.0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 1.0
220 100257 0 Cash loans F N Y 0 90000.000 312768.0 16119.0 270000.0 Unaccompanied Pensioner Secondary / secondary special Single / not married House / apartment 0.018634 -23090 365243 -7062.0 -2979 NaN 1 0 0 1 1 0 NaN 1.0 2 2 TUESDAY 10 0 0 0 0 0 0 XNA NaN 0.518701 0.730987 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 3.0 1.0 3.0 1.0 -2568.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 3.0
221 100258 0 Cash loans F N Y 0 135000.000 81000.0 9135.0 81000.0 Unaccompanied Working Secondary / secondary special Civil marriage House / apartment 0.025164 -16273 -1605 -939.0 -1092 NaN 1 1 0 1 0 0 Cooking staff 2.0 2 2 TUESDAY 14 0 0 0 0 0 0 Restaurant NaN 0.233865 NaN 0.1247 0.0563 0.9732 0.6328 0.1041 0.0000 0.2069 0.1667 0.2083 0.0000 0.0925 0.1388 0.0425 0.0869 0.1271 0.0584 0.9732 0.6472 0.1050 0.0000 0.2069 0.1667 0.2083 0.0000 0.1010 0.1446 0.0428 0.0920 0.1260 0.0563 0.9732 0.6377 0.1047 0.00 0.2069 0.1667 0.2083 0.0000 0.0941 0.1413 0.0427 0.0887 reg oper account block of flats 0.1850 Block No 6.0 0.0 6.0 0.0 -364.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NaN NaN NaN NaN NaN NaN
222 100259 0 Cash loans M Y Y 0 180000.000 427500.0 21955.5 427500.0 Unaccompanied Pensioner Secondary / secondary special Married House / apartment 0.018209 -20672 365243 -3315.0 -3299 25.0 1 0 0 1 0 0 NaN 2.0 3 3 FRIDAY 8 0 0 0 0 0 0 XNA NaN 0.379162 0.379100 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 2.0 0.0 2.0 0.0 -1838.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 4.0
223 100260 0 Cash loans M N Y 0 180000.000 180000.0 20488.5 180000.0 Unaccompanied Working Higher education Single / not married Co-op apartment 0.011657 -10038 -1746 -4742.0 -2723 NaN 1 1 1 1 0 0 Core staff 1.0 1 1 FRIDAY 14 0 1 1 0 1 1 Government NaN 0.705770 0.477649 0.0619 0.0760 0.9876 0.8300 0.0575 0.0000 0.1034 0.1667 0.2083 0.0559 0.0504 0.0703 0.0000 0.1447 0.0630 0.0788 0.9876 0.8367 0.0581 0.0000 0.1034 0.1667 0.2083 0.0572 0.0551 0.0733 0.0000 0.1532 0.0625 0.0760 0.9876 0.8323 0.0579 0.00 0.1034 0.1667 0.2083 0.0569 0.0513 0.0716 0.0000 0.1477 reg oper account block of flats 0.0868 Panel No 2.0 0.0 2.0 0.0 -770.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 2.0
224 100261 0 Cash loans F N Y 0 90000.000 522000.0 22243.5 522000.0 Unaccompanied Commercial associate Secondary / secondary special Married House / apartment 0.026392 -14974 -2389 -7478.0 -4099 NaN 1 1 1 1 0 0 Laborers 2.0 2 2 SATURDAY 11 0 0 0 0 1 1 Postal NaN 0.508092 0.542445 0.0278 NaN 0.9896 NaN NaN 0.0000 0.0690 0.0833 NaN NaN NaN 0.0254 NaN 0.0524 0.0284 NaN 0.9896 NaN NaN 0.0000 0.0690 0.0833 NaN NaN NaN 0.0265 NaN 0.0555 0.0281 NaN 0.9896 NaN NaN 0.00 0.0690 0.0833 NaN NaN NaN 0.0259 NaN 0.0535 NaN block of flats 0.0314 Stone, brick No 0.0 0.0 0.0 0.0 -2649.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 1.0
225 100262 0 Cash loans M Y Y 0 315000.000 1575000.0 41548.5 1575000.0 Family Working Secondary / secondary special Married House / apartment 0.008625 -13640 -3339 -3522.0 -5525 2.0 1 1 1 1 1 0 Drivers 2.0 2 2 SATURDAY 10 1 1 0 1 1 0 Postal 0.118684 0.690458 0.520898 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 0.0 0.0 0.0 0.0 -830.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 1.0 1.0
226 100263 0 Cash loans F N Y 0 108000.000 585000.0 32665.5 585000.0 Family Pensioner Higher education Widow House / apartment 0.009549 -23456 365243 -2267.0 -4483 NaN 1 0 0 1 1 0 NaN 1.0 2 2 THURSDAY 17 0 0 0 0 0 0 XNA 0.770814 0.235314 0.270707 0.0588 NaN 0.9930 NaN NaN 0.0000 0.1034 0.1667 NaN 0.0659 NaN 0.0651 NaN 0.0000 0.0599 NaN 0.9930 NaN NaN 0.0000 0.1034 0.1667 NaN 0.0674 NaN 0.0678 NaN 0.0000 0.0593 NaN 0.9930 NaN NaN 0.00 0.1034 0.1667 NaN 0.0671 NaN 0.0662 NaN 0.0000 NaN block of flats 0.0645 Panel No 0.0 0.0 0.0 0.0 -2255.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 0.0
227 100264 0 Cash loans F N Y 0 121500.000 1215000.0 48186.0 1215000.0 Unaccompanied Pensioner Higher education Married House / apartment 0.018209 -23037 365243 -2017.0 -4082 NaN 1 0 0 1 0 0 NaN 2.0 3 3 SATURDAY 7 0 0 0 0 0 0 XNA NaN 0.305150 0.695622 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 0.0 0.0 0.0 0.0 -2435.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 0.0
228 100265 0 Cash loans F N Y 0 112500.000 723996.0 34960.5 585000.0 Unaccompanied Commercial associate Secondary / secondary special Single / not married House / apartment 0.019689 -8302 -278 -1219.0 -959 NaN 1 1 0 1 1 0 Sales staff 1.0 2 2 TUESDAY 15 0 0 0 0 0 0 Self-employed 0.389138 0.577718 0.394495 0.0320 0.0229 0.9742 0.6464 0.0037 0.0000 0.0690 0.1250 0.1667 0.0448 0.0252 0.0204 0.0039 0.0020 0.0326 0.0238 0.9742 0.6602 0.0037 0.0000 0.0690 0.1250 0.1667 0.0458 0.0275 0.0213 0.0039 0.0022 0.0323 0.0229 0.9742 0.6511 0.0037 0.00 0.0690 0.1250 0.1667 0.0456 0.0257 0.0208 0.0039 0.0021 reg oper account block of flats 0.0186 Block No 3.0 0.0 3.0 0.0 -800.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 2.0
229 100266 0 Cash loans F N N 0 157500.000 1256400.0 36864.0 900000.0 Unaccompanied Commercial associate Secondary / secondary special Widow Municipal apartment 0.032561 -18810 -995 -9761.0 -2290 NaN 1 1 0 1 0 0 NaN 1.0 1 1 FRIDAY 17 0 0 0 0 0 0 Business Entity Type 3 NaN 0.695817 0.713631 0.0619 0.0612 0.9737 NaN NaN 0.0000 0.1379 0.1667 NaN 0.0739 NaN 0.0547 NaN 0.0000 0.0630 0.0635 0.9737 NaN NaN 0.0000 0.1379 0.1667 NaN 0.0756 NaN 0.0570 NaN 0.0000 0.0625 0.0612 0.9737 NaN NaN 0.00 0.1379 0.1667 NaN 0.0752 NaN 0.0557 NaN 0.0000 NaN block of flats 0.0430 Panel No 2.0 1.0 2.0 1.0 -1388.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 1.0
230 100267 0 Cash loans M N Y 0 225000.000 675000.0 17937.0 675000.0 Unaccompanied Commercial associate Higher education Married House / apartment 0.026392 -13685 -3281 -7727.0 -4477 NaN 1 1 1 1 1 0 Managers 2.0 2 2 TUESDAY 18 0 0 0 0 0 0 Business Entity Type 3 NaN 0.681537 0.631355 0.1856 NaN 0.9821 NaN NaN 0.2000 0.1724 0.3333 NaN NaN NaN 0.1909 NaN 0.0000 0.1891 NaN 0.9821 NaN NaN 0.2014 0.1724 0.3333 NaN NaN NaN 0.1989 NaN 0.0000 0.1874 NaN 0.9821 NaN NaN 0.20 0.1724 0.3333 NaN NaN NaN 0.1944 NaN 0.0000 NaN block of flats 0.1502 Panel No 0.0 0.0 0.0 0.0 -330.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 1.0 3.0
231 100268 0 Cash loans F N N 0 85500.000 225000.0 18040.5 225000.0 Unaccompanied Working Secondary / secondary special Married House / apartment 0.014464 -10571 -1028 -643.0 -1899 NaN 1 1 0 1 0 0 Laborers 2.0 2 2 TUESDAY 12 0 0 0 0 0 0 Business Entity Type 3 0.101045 0.646689 0.225087 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 0.0 0.0 0.0 0.0 -2206.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 2.0 3.0
232 100269 0 Cash loans M N Y 0 180000.000 427450.5 23319.0 369000.0 Unaccompanied Commercial associate Secondary / secondary special Married House / apartment 0.010276 -11180 -1313 -4774.0 -3699 NaN 1 1 0 1 0 0 Laborers 2.0 2 2 THURSDAY 9 0 0 0 1 1 0 Business Entity Type 3 NaN 0.285898 0.448962 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 0.0 0.0 0.0 0.0 -177.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 1.0 2.0
233 100270 0 Revolving loans F N Y 0 90000.000 337500.0 16875.0 337500.0 Unaccompanied Pensioner Higher education Married House / apartment 0.032561 -21497 365243 -14502.0 -4288 NaN 1 0 0 1 1 0 NaN 2.0 1 1 TUESDAY 16 0 0 0 0 0 0 XNA NaN 0.714056 0.750375 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 0.0 0.0 0.0 0.0 -2124.0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 3.0
234 100272 0 Revolving loans F N Y 1 135000.000 180000.0 9000.0 180000.0 Spouse, partner Working Secondary / secondary special Married House / apartment 0.010006 -9135 -460 -1219.0 -1815 NaN 1 1 1 1 1 0 Sales staff 3.0 2 1 FRIDAY 14 0 0 0 0 0 0 Business Entity Type 2 NaN 0.195258 0.183035 0.1103 0.0640 0.9841 0.7824 0.0308 0.0800 0.0345 0.2917 0.3333 0.0148 0.0765 0.0635 0.0618 0.0361 0.1124 0.0664 0.9841 0.7909 0.0311 0.0806 0.0345 0.2917 0.3333 0.0151 0.0836 0.0662 0.0623 0.0382 0.1114 0.0640 0.9841 0.7853 0.0310 0.08 0.0345 0.2917 0.3333 0.0150 0.0778 0.0646 0.0621 0.0369 reg oper account block of flats 0.0585 Panel No 3.0 1.0 3.0 1.0 -70.0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 1.0 2.0
235 100273 1 Cash loans F N Y 0 157500.000 1710000.0 66262.5 1710000.0 Unaccompanied Working Secondary / secondary special Married House / apartment 0.004849 -23127 -9274 -817.0 -4173 NaN 1 1 0 1 0 0 Medicine staff 2.0 2 2 WEDNESDAY 10 0 0 0 0 0 0 Other NaN 0.126332 NaN 0.0330 NaN 0.9757 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 0.0336 NaN 0.9757 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 0.0333 NaN 0.9757 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN block of flats 0.0195 Stone, brick No 0.0 0.0 0.0 0.0 -1246.0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NaN NaN NaN NaN NaN NaN
236 100274 0 Cash loans M Y Y 0 308250.000 1305000.0 38281.5 1305000.0 Family Working Secondary / secondary special Civil marriage House / apartment 0.026392 -15612 -1106 -8060.0 -4018 6.0 1 1 1 1 1 0 Drivers 2.0 2 2 MONDAY 14 0 0 0 0 0 0 Self-employed 0.652943 0.698453 0.800451 0.1979 0.1629 0.9851 NaN NaN 0.2400 0.2069 0.3333 NaN NaN NaN 0.2063 NaN 0.1504 0.2017 0.1690 0.9851 NaN NaN 0.2417 0.2069 0.3333 NaN NaN NaN 0.2149 NaN 0.1592 0.1999 0.1629 0.9851 NaN NaN 0.24 0.2069 0.3333 NaN NaN NaN 0.2100 NaN 0.1535 NaN block of flats 0.1949 Panel No 1.0 0.0 1.0 0.0 -2263.0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 3.0
237 100275 0 Cash loans F Y Y 0 108000.000 646920.0 18670.5 540000.0 Unaccompanied Pensioner Secondary / secondary special Widow House / apartment 0.019101 -23650 365243 -11380.0 -4923 64.0 1 0 0 1 0 0 NaN 1.0 2 2 SUNDAY 13 0 0 0 0 0 0 XNA NaN 0.731907 0.720944 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 0.0 0.0 0.0 0.0 -1563.0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 3.0
238 100276 0 Cash loans F Y Y 0 135000.000 497520.0 39438.0 450000.0 Family Working Secondary / secondary special Married House / apartment 0.025164 -12246 -1787 -1002.0 -4898 22.0 1 1 0 1 0 0 Private service staff 2.0 2 2 SATURDAY 11 0 0 0 0 0 0 Services 0.531800 0.216849 NaN 0.0825 0.0796 0.9747 0.6532 0.0496 0.0000 0.1379 0.1667 0.2083 0.0741 0.0672 0.0680 0.0039 0.0050 0.0840 0.0826 0.9747 0.6668 0.0501 0.0000 0.1379 0.1667 0.2083 0.0758 0.0735 0.0704 0.0039 0.0053 0.0833 0.0796 0.9747 0.6578 0.0499 0.00 0.1379 0.1667 0.2083 0.0754 0.0684 0.0692 0.0039 0.0051 reg oper account block of flats 0.0600 Panel No 2.0 0.0 2.0 0.0 0.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 1.0
239 100277 0 Cash loans M Y N 0 67500.000 364896.0 16200.0 315000.0 Unaccompanied Pensioner Secondary / secondary special Married House / apartment 0.006008 -21812 365243 -12539.0 -5030 13.0 1 0 0 1 1 0 NaN 2.0 2 2 THURSDAY 19 0 0 0 0 0 0 XNA 0.856740 0.574514 NaN NaN NaN 0.9866 NaN NaN 0.0000 0.1034 0.0833 NaN NaN NaN 0.0255 NaN 0.0056 NaN NaN 0.9866 NaN NaN 0.0000 0.1034 0.0833 NaN NaN NaN 0.0266 NaN 0.0060 NaN NaN 0.9866 NaN NaN 0.00 0.1034 0.0833 NaN NaN NaN 0.0260 NaN 0.0057 NaN block of flats 0.0213 NaN No 1.0 0.0 1.0 0.0 -318.0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 NaN NaN NaN NaN NaN NaN
240 100279 0 Cash loans M N Y 0 180000.000 254700.0 16407.0 225000.0 Unaccompanied Pensioner Secondary / secondary special Widow House / apartment 0.072508 -23946 365243 -6935.0 -3948 NaN 1 0 0 1 0 0 NaN 1.0 1 1 WEDNESDAY 16 0 0 0 0 0 0 XNA NaN 0.458685 0.528093 0.3041 0.1854 0.9791 0.7144 0.0000 0.3200 0.2759 0.3333 0.0417 0.0000 0.2412 0.2810 0.0309 0.0090 0.3099 0.1924 0.9791 0.7256 0.0000 0.3222 0.2759 0.3333 0.0417 0.0000 0.2635 0.2928 0.0311 0.0096 0.3071 0.1854 0.9791 0.7182 0.0000 0.32 0.2759 0.3333 0.0417 0.0000 0.2454 0.2861 0.0311 0.0092 reg oper account block of flats 0.2230 Panel No 0.0 0.0 0.0 0.0 -975.0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 1.0 5.0
241 100281 0 Cash loans F N Y 1 112500.000 675000.0 21906.0 675000.0 Unaccompanied Commercial associate Secondary / secondary special Widow House / apartment 0.025164 -17957 -4942 -7868.0 -1478 NaN 1 1 1 1 0 0 Sales staff 2.0 2 2 THURSDAY 15 0 0 0 0 0 0 Self-employed NaN 0.660907 0.622922 0.0124 0.0000 0.9702 0.5920 0.0017 0.0000 0.0690 0.0417 NaN 0.0093 0.0101 0.0104 0.0000 0.0000 0.0126 0.0000 0.9702 0.6080 0.0017 0.0000 0.0690 0.0417 NaN 0.0095 0.0110 0.0108 0.0000 0.0000 0.0125 0.0000 0.9702 0.5975 0.0017 0.00 0.0690 0.0417 NaN 0.0095 0.0103 0.0105 0.0000 0.0000 not specified block of flats 0.0091 Wooden No 0.0 0.0 0.0 0.0 -1631.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 1.0 0.0 1.0
242 100282 1 Revolving loans F Y Y 0 73341.000 135000.0 6750.0 135000.0 Unaccompanied Commercial associate Higher education Single / not married House / apartment 0.018801 -9157 -146 -2926.0 -1822 11.0 1 1 0 1 0 0 Managers 1.0 2 2 THURSDAY 11 0 0 0 0 0 0 Insurance NaN 0.523230 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN No 1.0 1.0 1.0 1.0 -64.0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 NaN NaN NaN NaN NaN NaN
243 100283 0 Cash loans F N Y 0 135000.000 747000.0 38268.0 747000.0 Unaccompanied Commercial associate Secondary / secondary special Civil marriage House / apartment 0.026392 -12585 -870 -6120.0 -4734 NaN 1 1 0 1 0 0 Sales staff 2.0 2 2 SATURDAY 10 0 0 0 0 0 0 Self-employed NaN 0.653572 0.832785 0.0825 NaN 0.9737 NaN NaN 0.0000 0.1379 0.1667 NaN NaN NaN 0.0644 NaN 0.0000 0.0840 NaN 0.9737 NaN NaN 0.0000 0.1379 0.1667 NaN NaN NaN 0.0671 NaN 0.0000 0.0833 NaN 0.9737 NaN NaN 0.00 0.1379 0.1667 NaN NaN NaN 0.0656 NaN 0.0000 NaN block of flats 0.0506 Stone, brick No 4.0 1.0 4.0 1.0 -914.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 1.0 0.0 0.0 1.0
244 100284 0 Cash loans M Y Y 2 180000.000 675000.0 29862.0 675000.0 Unaccompanied Working Higher education Married With parents 0.015221 -14445 -3218 -5173.0 -5186 2.0 1 1 0 1 1 1 Laborers 4.0 2 2 TUESDAY 16 0 0 0 0 0 0 Business Entity Type 3 0.692455 0.328269 0.758393 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 0.0 0.0 0.0 0.0 0.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 1.0 0.0 3.0
245 100285 0 Revolving loans F N Y 0 112500.000 180000.0 9000.0 180000.0 Spouse, partner Working Secondary / secondary special Civil marriage Rented apartment 0.028663 -10755 -1185 -765.0 -3443 NaN 1 1 0 1 0 0 Cooking staff 2.0 2 2 THURSDAY 13 0 0 0 1 1 0 Self-employed NaN 0.435413 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 0.0 0.0 0.0 0.0 0.0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 2.0
246 100286 1 Cash loans M Y Y 1 121500.000 263686.5 17298.0 238500.0 Spouse, partner Working Secondary / secondary special Married House / apartment 0.014464 -17847 -448 -3595.0 -1381 21.0 1 1 0 1 0 0 Drivers 3.0 2 2 SATURDAY 5 0 0 0 0 0 0 Business Entity Type 3 NaN 0.710925 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 1.0 0.0 1.0 0.0 -238.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 0.0
247 100287 0 Cash loans F N Y 0 202500.000 225000.0 22383.0 225000.0 Family Pensioner Higher education Married House / apartment 0.026392 -24891 365243 -2120.0 -2019 NaN 1 0 0 1 1 0 NaN 2.0 2 2 TUESDAY 15 0 0 0 0 0 0 XNA NaN 0.655530 NaN 0.1825 NaN 0.9950 NaN NaN 0.2000 0.1724 0.3333 NaN NaN NaN 0.2495 NaN 0.3146 0.1859 NaN 0.9950 NaN NaN 0.2014 0.1724 0.3333 NaN NaN NaN 0.2599 NaN 0.3330 0.1842 NaN 0.9950 NaN NaN 0.20 0.1724 0.3333 NaN NaN NaN 0.2539 NaN 0.3212 NaN block of flats 0.2646 Stone, brick No 5.0 0.0 5.0 0.0 -648.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NaN NaN NaN NaN NaN NaN
248 100288 0 Cash loans F N N 0 157500.000 450000.0 21888.0 450000.0 Unaccompanied Working Secondary / secondary special Civil marriage House / apartment 0.010032 -9907 -648 -5628.0 -2567 NaN 1 1 0 1 0 0 Laborers 2.0 2 2 SATURDAY 6 0 1 1 0 1 1 Other 0.237532 0.597181 NaN 0.0619 0.0635 0.9821 0.7552 0.0334 0.0000 0.1379 0.1667 0.2083 0.0511 0.0496 0.0502 0.0039 0.0042 0.0630 0.0659 0.9821 0.7648 0.0337 0.0000 0.1379 0.1667 0.2083 0.0523 0.0542 0.0523 0.0039 0.0045 0.0625 0.0635 0.9821 0.7585 0.0336 0.00 0.1379 0.1667 0.2083 0.0520 0.0504 0.0511 0.0039 0.0043 reg oper account block of flats 0.0586 Panel No 0.0 0.0 0.0 0.0 -425.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NaN NaN NaN NaN NaN NaN
249 100289 0 Cash loans M Y N 0 202500.000 526491.0 26878.5 454500.0 Unaccompanied Working Higher education Married House / apartment 0.022625 -14811 -7668 -1761.0 -4268 0.0 1 1 1 1 1 0 Managers 2.0 2 2 TUESDAY 15 0 0 0 0 0 0 Transport: type 4 0.645151 0.658704 0.697147 0.4072 0.2893 0.9816 0.7484 0.0089 0.4400 0.3793 0.3333 0.3750 0.1754 0.3261 0.4133 0.0270 0.0083 0.4149 0.3002 0.9816 0.7583 0.0090 0.4431 0.3793 0.3333 0.3750 0.1794 0.3563 0.4306 0.0272 0.0087 0.4112 0.2893 0.9816 0.7518 0.0090 0.44 0.3793 0.3333 0.3750 0.1784 0.3318 0.4207 0.0272 0.0084 NaN block of flats 0.3288 Panel No 0.0 0.0 0.0 0.0 -669.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 1.0 2.0
250 100290 0 Cash loans M N N 0 135000.000 224136.0 17707.5 198000.0 Unaccompanied Working Secondary / secondary special Separated House / apartment 0.022625 -14726 -142 -3458.0 -2690 NaN 1 1 0 1 0 1 Core staff 1.0 2 2 THURSDAY 17 0 0 0 0 0 0 Business Entity Type 3 0.405560 0.258728 0.237916 0.1237 0.0644 0.9921 NaN NaN 0.1200 0.1034 0.3750 NaN 0.0384 NaN 0.1263 NaN 0.0000 0.1261 0.0669 0.9921 NaN NaN 0.1208 0.1034 0.3750 NaN 0.0392 NaN 0.1316 NaN 0.0000 0.1249 0.0644 0.9921 NaN NaN 0.12 0.1034 0.3750 NaN 0.0390 NaN 0.1286 NaN 0.0000 NaN block of flats 0.1116 Panel No 0.0 0.0 0.0 0.0 -1053.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 3.0 6.0
251 100291 0 Cash loans F N N 1 153000.000 292500.0 14355.0 292500.0 Unaccompanied Working Incomplete higher Married House / apartment 0.015221 -11881 -716 -2283.0 -4417 NaN 1 1 1 1 1 1 Sales staff 3.0 2 2 TUESDAY 17 0 0 0 0 0 0 Self-employed NaN 0.182413 0.331251 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 0.0 0.0 0.0 0.0 -502.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 3.0
252 100292 0 Cash loans F N Y 0 202500.000 675000.0 29862.0 675000.0 Unaccompanied Pensioner Secondary / secondary special Married House / apartment 0.028663 -21185 365243 -1702.0 -4229 NaN 1 0 0 1 0 1 NaN 2.0 2 2 WEDNESDAY 11 0 0 0 0 0 0 XNA NaN 0.065452 0.549597 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 3.0 2.0 3.0 1.0 0.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 2.0
253 100293 0 Cash loans F N Y 0 202500.000 1271281.5 65956.5 1201500.0 Family Working Higher education Married House / apartment 0.019101 -14247 -2993 -3842.0 -4142 NaN 1 1 0 1 0 0 Managers 2.0 2 2 MONDAY 18 0 0 0 0 0 0 Transport: type 4 0.450264 0.495546 0.612704 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 1.0 1.0 1.0 1.0 0.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 4.0
254 100294 0 Cash loans F N Y 0 157500.000 835380.0 35392.5 675000.0 Unaccompanied Commercial associate Secondary / secondary special Widow House / apartment 0.009630 -20984 -7148 -8781.0 -3962 NaN 1 1 0 1 1 0 Core staff 1.0 2 2 FRIDAY 11 0 0 0 0 0 0 Transport: type 2 NaN 0.441383 0.546023 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 0.0 0.0 0.0 0.0 -16.0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 1.0
255 100295 1 Cash loans M Y N 1 225000.000 1019205.0 31032.0 774000.0 Unaccompanied Commercial associate Secondary / secondary special Single / not married House / apartment 0.072508 -11356 -602 -335.0 -3224 9.0 1 1 0 1 0 0 Laborers 2.0 1 1 MONDAY 14 1 0 1 0 0 0 Business Entity Type 3 0.262005 0.302394 0.463275 0.2402 0.1098 0.9916 0.8844 0.4682 0.4000 0.1724 0.5417 0.5000 0.0223 0.1942 0.2270 0.0077 0.0075 0.2447 0.1139 0.9916 0.8889 0.4724 0.4028 0.1724 0.5417 0.5000 0.0228 0.2121 0.2365 0.0078 0.0080 0.2425 0.1098 0.9916 0.8859 0.4711 0.40 0.1724 0.5417 0.5000 0.0227 0.1975 0.2310 0.0078 0.0077 reg oper account block of flats 0.1874 Panel No 3.0 0.0 3.0 0.0 -14.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0.0 0.0 0.0 6.0 0.0 1.0
256 100296 0 Cash loans M N Y 1 225000.000 223884.0 24241.5 202500.0 Unaccompanied Commercial associate Secondary / secondary special Married House / apartment 0.046220 -17186 -1116 -4258.0 -730 NaN 1 1 0 1 0 1 Drivers 3.0 1 1 SUNDAY 13 0 1 1 0 0 0 Business Entity Type 3 0.602752 0.711792 NaN 0.1649 0.0000 0.9881 0.8368 0.0374 0.1600 0.1379 0.3750 0.4167 0.2082 0.1345 0.1643 0.0000 0.0000 0.1681 0.0000 0.9881 0.8432 0.0378 0.1611 0.1379 0.3750 0.4167 0.2129 0.1469 0.1712 0.0000 0.0000 0.1665 0.0000 0.9881 0.8390 0.0377 0.16 0.1379 0.3750 0.4167 0.2118 0.1368 0.1673 0.0000 0.0000 reg oper account block of flats 0.1672 Panel No 0.0 0.0 0.0 0.0 -231.0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 NaN NaN NaN NaN NaN NaN
257 100297 0 Cash loans M Y N 0 135000.000 334152.0 17059.5 270000.0 Family Working Secondary / secondary special Married House / apartment 0.026392 -10427 -1274 -2609.0 -3027 9.0 1 1 0 1 0 0 Drivers 2.0 2 2 MONDAY 18 0 0 0 0 0 0 Self-employed NaN 0.612623 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 2.0 1.0 2.0 0.0 -1002.0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 NaN NaN NaN NaN NaN NaN
258 100298 0 Cash loans F N Y 0 270000.000 473760.0 48676.5 450000.0 Unaccompanied Working Secondary / secondary special Married House / apartment 0.020713 -16223 -3077 -3425.0 -5501 NaN 1 1 0 1 0 0 Managers 2.0 3 1 THURSDAY 10 0 0 0 0 0 0 Self-employed NaN 0.492311 NaN 0.1959 0.3627 0.9712 0.6056 0.0377 0.0000 0.3793 0.1667 0.0417 0.1716 0.1513 0.2448 0.0386 0.1504 0.1996 0.3764 0.9712 0.6210 0.0380 0.0000 0.3793 0.1667 0.0417 0.1755 0.1653 0.2550 0.0389 0.1593 0.1978 0.3627 0.9712 0.6109 0.0379 0.00 0.3793 0.1667 0.0417 0.1746 0.1539 0.2492 0.0388 0.1536 reg oper account block of flats 0.2252 Stone, brick No 0.0 0.0 0.0 0.0 -1485.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 4.0
259 100299 0 Revolving loans F N Y 0 112500.000 540000.0 27000.0 540000.0 Unaccompanied State servant Secondary / secondary special Married House / apartment 0.006008 -19045 -11916 -6644.0 -2566 NaN 1 1 0 1 0 0 Medicine staff 2.0 2 2 SATURDAY 12 0 0 0 1 1 0 Medicine NaN 0.704761 0.588488 0.0464 0.0712 0.9930 0.9048 0.0303 0.0000 0.1207 0.1667 0.2083 NaN 0.0378 0.0554 0.0000 0.0000 0.0315 0.0694 0.9881 0.8432 0.0306 0.0000 0.1034 0.1667 0.2083 NaN 0.0275 0.0577 0.0000 0.0000 0.0468 0.0712 0.9930 0.9061 0.0305 0.00 0.1207 0.1667 0.2083 NaN 0.0385 0.0564 0.0000 0.0000 reg oper account block of flats 0.0602 Stone, brick No 6.0 0.0 6.0 0.0 -1393.0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 3.0
260 100300 1 Cash loans M N N 0 63000.000 426645.0 22468.5 324000.0 Other_A Commercial associate Secondary / secondary special Single / not married House / apartment 0.018634 -12209 -1572 -6348.0 -4551 NaN 1 1 0 1 1 0 Sales staff 1.0 2 2 WEDNESDAY 10 0 0 0 0 0 0 Trade: type 3 NaN 0.474892 0.315472 0.1041 0.1126 0.9806 0.7348 0.0012 0.0000 0.2069 0.1667 0.2083 0.0195 0.0841 0.0900 0.0039 0.0646 0.1061 0.1168 0.9806 0.7452 0.0012 0.0000 0.2069 0.1667 0.2083 0.0200 0.0918 0.0937 0.0039 0.0684 0.1051 0.1126 0.9806 0.7383 0.0012 0.00 0.2069 0.1667 0.2083 0.0199 0.0855 0.0916 0.0039 0.0660 reg oper spec account block of flats 0.0855 Stone, brick No 3.0 1.0 3.0 1.0 -2149.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 0.0
261 100301 1 Cash loans M N Y 1 112500.000 571486.5 31131.0 454500.0 Unaccompanied Working Secondary / secondary special Married House / apartment 0.022625 -8292 -166 -3062.0 -16 NaN 1 1 0 1 0 0 Laborers 3.0 2 2 MONDAY 11 0 0 0 0 0 0 Business Entity Type 3 NaN 0.253289 0.200926 0.0737 0.1016 0.4933 0.8232 0.0632 0.0800 0.1379 0.3333 0.0417 0.0405 0.0000 0.1469 0.0000 0.0038 0.0000 0.1054 0.0000 0.8301 0.0637 0.0000 0.1379 0.3333 0.0417 0.0414 0.0000 0.1531 0.0000 0.0040 0.0744 0.1016 0.4933 0.8256 0.0636 0.08 0.1379 0.3333 0.0417 0.0412 0.0000 0.1496 0.0000 0.0039 reg oper spec account block of flats 0.0000 Panel No 0.0 0.0 0.0 0.0 0.0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1.0 0.0 0.0 1.0 0.0 2.0
262 100302 0 Cash loans F N Y 0 180000.000 187704.0 9256.5 148500.0 Unaccompanied Working Secondary / secondary special Civil marriage House / apartment 0.019689 -9105 -482 -5874.0 -1139 NaN 1 1 0 1 0 0 Sales staff 2.0 2 2 WEDNESDAY 11 0 0 0 0 1 1 Bank 0.341851 0.479431 0.051682 0.0959 0.0925 0.9871 0.8232 0.0152 0.0000 0.2069 0.1667 0.2083 0.1110 0.0748 0.0759 0.0154 0.0723 0.0977 0.0960 0.9871 0.8301 0.0153 0.0000 0.2069 0.1667 0.2083 0.1136 0.0817 0.0791 0.0156 0.0765 0.0968 0.0925 0.9871 0.8256 0.0153 0.00 0.2069 0.1667 0.2083 0.1130 0.0761 0.0773 0.0155 0.0738 reg oper account block of flats 0.0837 Panel No 0.0 0.0 0.0 0.0 -930.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 2.0 3.0
263 100303 0 Cash loans F N Y 0 292500.000 254700.0 15709.5 225000.0 Unaccompanied Pensioner Secondary / secondary special Widow House / apartment 0.016612 -24006 365243 -2239.0 -4447 NaN 1 0 0 1 0 0 NaN 1.0 2 2 WEDNESDAY 10 0 0 0 0 0 0 XNA NaN 0.282490 0.410103 0.0742 0.0306 0.9876 0.8300 0.0182 0.0800 0.0517 0.4792 0.5208 0.1202 0.0605 0.1177 0.0000 0.0000 0.0756 0.0317 0.9786 0.7190 0.0184 0.0806 0.0345 0.3333 0.3750 0.1230 0.0661 0.0738 0.0000 0.0000 0.0749 0.0306 0.9876 0.8323 0.0183 0.08 0.0517 0.4792 0.5208 0.1223 0.0616 0.1198 0.0000 0.0000 reg oper account block of flats 0.0657 Stone, brick No 1.0 0.0 1.0 0.0 -609.0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 6.0
264 100304 0 Revolving loans F N Y 0 225000.000 675000.0 33750.0 675000.0 Unaccompanied Working Secondary / secondary special Single / not married House / apartment 0.008019 -22794 -15632 -7959.0 -4311 NaN 1 1 0 1 1 0 NaN 1.0 2 2 TUESDAY 13 0 0 0 0 0 0 Business Entity Type 3 NaN 0.654943 0.795803 0.0464 0.0556 0.9891 0.8504 0.0247 0.0000 0.1034 0.1667 0.2083 0.0386 0.0378 0.0548 0.0000 0.0000 0.0473 0.0577 0.9891 0.8563 0.0249 0.0000 0.1034 0.1667 0.2083 0.0395 0.0413 0.0571 0.0000 0.0000 0.0468 0.0556 0.9891 0.8524 0.0249 0.00 0.1034 0.1667 0.2083 0.0393 0.0385 0.0558 0.0000 0.0000 not specified block of flats 0.0566 Stone, brick No 0.0 0.0 0.0 0.0 -1121.0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 0.0
265 100305 0 Cash loans M Y Y 0 135000.000 237024.0 15970.5 180000.0 Spouse, partner Working Incomplete higher Single / not married House / apartment 0.020713 -9011 -100 -7897.0 -1654 15.0 1 1 0 1 0 1 Sales staff 1.0 3 3 SATURDAY 6 0 0 0 0 0 0 Business Entity Type 3 0.158197 0.434225 NaN 0.0186 0.0487 0.9727 0.6260 0.0340 0.0000 0.0690 0.0833 0.1250 0.0184 0.0151 0.0220 0.0000 0.0000 0.0189 0.0505 0.9727 0.6406 0.0343 0.0000 0.0690 0.0833 0.1250 0.0188 0.0165 0.0229 0.0000 0.0000 0.0187 0.0487 0.9727 0.6310 0.0343 0.00 0.0690 0.0833 0.1250 0.0187 0.0154 0.0224 0.0000 0.0000 reg oper account block of flats 0.0173 Stone, brick No 1.0 0.0 1.0 0.0 -945.0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 NaN NaN NaN NaN NaN NaN
266 100307 0 Cash loans M N Y 0 225000.000 451804.5 35824.5 369000.0 Unaccompanied State servant Secondary / secondary special Married House / apartment 0.018850 -16546 -1480 -2196.0 -7 NaN 1 1 0 1 0 0 Managers 2.0 2 2 MONDAY 11 0 1 1 0 1 1 Military 0.572585 0.680321 0.448962 0.2773 0.1882 0.9771 0.6872 NaN 0.0400 0.1724 0.3333 NaN NaN NaN 0.2404 NaN 0.0931 0.2826 0.1953 0.9772 0.6994 NaN 0.0403 0.1724 0.3333 NaN NaN NaN 0.2504 NaN 0.0986 0.2800 0.1882 0.9771 0.6914 NaN 0.04 0.1724 0.3333 NaN NaN NaN 0.2447 NaN 0.0951 NaN block of flats 0.2093 NaN No 2.0 1.0 2.0 0.0 -745.0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 4.0
267 100308 0 Cash loans M N N 0 157500.000 208512.0 21892.5 180000.0 Unaccompanied Working Higher education Married House / apartment 0.022625 -14534 -3397 -3776.0 -5113 NaN 1 1 1 1 0 0 Sales staff 2.0 2 2 MONDAY 10 0 0 0 0 0 0 Business Entity Type 3 0.876653 0.728456 0.497469 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 0.0 0.0 0.0 0.0 -1891.0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 2.0 2.0
268 100309 0 Cash loans F N Y 0 157500.000 263686.5 15268.5 238500.0 Unaccompanied State servant Secondary / secondary special Married House / apartment 0.010500 -16098 -3131 -7189.0 -4143 NaN 1 1 0 1 0 0 Laborers 2.0 3 3 MONDAY 9 0 0 0 0 0 0 Postal 0.493886 0.334169 NaN 0.0722 0.0036 0.9781 0.7008 0.0079 0.0000 0.1379 0.1667 0.0417 0.0841 0.0588 0.0626 0.0000 0.0000 0.0735 0.0038 0.9782 0.7125 0.0080 0.0000 0.1379 0.1667 0.0417 0.0860 0.0643 0.0652 0.0000 0.0000 0.0729 0.0036 0.9781 0.7048 0.0079 0.00 0.1379 0.1667 0.0417 0.0856 0.0599 0.0637 0.0000 0.0000 reg oper account block of flats 0.0492 Stone, brick No 0.0 0.0 0.0 0.0 -288.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 1.0 0.0 3.0
269 100310 0 Cash loans M N N 0 180000.000 1071000.0 31315.5 1071000.0 Unaccompanied Working Higher education Married Rented apartment 0.018029 -14659 -1228 -688.0 -1107 NaN 1 1 1 1 0 0 NaN 2.0 3 3 WEDNESDAY 13 0 0 0 1 1 1 Business Entity Type 3 NaN 0.160158 0.438281 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 0.0 0.0 0.0 0.0 -89.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 1.0 0.0 2.0
270 100313 0 Cash loans F N Y 0 87750.000 1206954.0 35419.5 945000.0 Unaccompanied Pensioner Higher education Married House / apartment 0.019101 -22510 365243 -6983.0 -4765 NaN 1 0 0 1 1 0 NaN 2.0 2 2 TUESDAY 10 0 0 0 0 0 0 XNA NaN 0.707841 0.664248 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 0.0 0.0 0.0 0.0 -2379.0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 1.0
271 100314 0 Cash loans M N N 2 292500.000 239850.0 25960.5 225000.0 Unaccompanied Working Secondary / secondary special Married House / apartment 0.018850 -10788 -2573 -5248.0 -3478 NaN 1 1 1 1 1 0 Laborers 4.0 2 2 TUESDAY 12 0 1 1 0 1 1 Self-employed 0.217720 0.780763 0.770087 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 0.0 0.0 0.0 0.0 -1073.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 0.0
272 100315 0 Cash loans F Y Y 1 121500.000 288873.0 21064.5 238500.0 Unaccompanied Working Higher education Married House / apartment 0.018029 -10203 -59 -6422.0 -2513 65.0 1 1 0 1 0 0 Sales staff 3.0 3 3 THURSDAY 10 0 0 0 0 0 0 Business Entity Type 2 0.264876 0.592295 0.495666 0.0361 NaN 0.9891 NaN NaN 0.0400 0.0345 0.3333 NaN NaN NaN 0.0411 NaN NaN 0.0368 NaN 0.9891 NaN NaN 0.0403 0.0345 0.3333 NaN NaN NaN 0.0428 NaN NaN 0.0364 NaN 0.9891 NaN NaN 0.04 0.0345 0.3333 NaN NaN NaN 0.0418 NaN NaN NaN block of flats 0.0323 Panel No 5.0 0.0 5.0 0.0 -1441.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 1.0
273 100316 0 Cash loans F N Y 0 67500.000 622413.0 26500.5 495000.0 Spouse, partner Working Secondary / secondary special Married House / apartment 0.005002 -15076 -4917 -1725.0 -2325 NaN 1 1 0 1 1 0 Core staff 2.0 3 3 THURSDAY 15 0 0 0 0 0 0 Medicine 0.567624 0.575925 0.689479 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 0.0 0.0 0.0 0.0 -1955.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 1.0 0.0 0.0 1.0
274 100317 0 Cash loans F N Y 0 180000.000 630000.0 30438.0 630000.0 Unaccompanied Working Higher education Married House / apartment 0.030755 -17013 -7426 -3274.0 -539 NaN 1 1 0 1 0 0 Core staff 2.0 2 2 WEDNESDAY 12 0 0 0 0 0 0 School NaN 0.708167 0.577969 0.0423 0.0487 0.9672 0.5376 NaN 0.0000 0.2069 0.1250 0.1667 0.0521 NaN 0.0453 NaN 0.0220 0.0431 0.0506 0.9672 0.5557 NaN 0.0000 0.2069 0.1250 0.1667 0.0533 NaN 0.0471 NaN 0.0233 0.0427 0.0487 0.9672 0.5438 NaN 0.00 0.2069 0.1250 0.1667 0.0530 NaN 0.0461 NaN 0.0224 reg oper account block of flats 0.0525 Stone, brick No 0.0 0.0 0.0 0.0 -1701.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 4.0
275 100318 0 Cash loans F N N 0 90000.000 381528.0 12109.5 315000.0 Unaccompanied Working Higher education Single / not married House / apartment 0.018209 -9627 -189 -709.0 -2301 NaN 1 1 0 1 0 0 Core staff 1.0 3 3 WEDNESDAY 12 0 0 0 0 0 0 Trade: type 7 0.646881 0.533415 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 0.0 0.0 0.0 0.0 -141.0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 0.0
276 100319 0 Cash loans F N Y 1 135000.000 781920.0 32868.0 675000.0 Unaccompanied Commercial associate Higher education Married House / apartment 0.022625 -11631 -782 -7868.0 -2992 NaN 1 1 1 1 1 0 NaN 3.0 2 2 THURSDAY 8 0 0 0 0 0 0 Business Entity Type 3 0.653368 0.565290 0.495666 0.0835 0.1024 0.9891 0.8504 0.0481 0.0000 0.1379 0.1667 0.2083 0.0987 0.0672 0.1035 0.0039 0.0010 0.0851 0.1063 0.9891 0.8563 0.0486 0.0000 0.1379 0.1667 0.2083 0.1009 0.0735 0.1078 0.0039 0.0010 0.0843 0.1024 0.9891 0.8524 0.0484 0.00 0.1379 0.1667 0.2083 0.1004 0.0684 0.1053 0.0039 0.0010 not specified block of flats 0.1077 Panel No 0.0 0.0 0.0 0.0 -2028.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 1.0
277 100320 0 Cash loans M Y Y 0 225000.000 481855.5 49374.0 463500.0 Spouse, partner Working Secondary / secondary special Married House / apartment 0.025164 -11996 -2907 -2562.0 -4623 6.0 1 1 0 1 0 0 High skill tech staff 2.0 2 2 FRIDAY 11 0 1 1 0 1 1 Construction NaN 0.610447 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 0.0 0.0 0.0 0.0 -842.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 1.0 0.0 2.0 3.0
278 100321 0 Cash loans M Y N 1 270000.000 521280.0 31500.0 450000.0 Unaccompanied Commercial associate Secondary / secondary special Civil marriage House / apartment 0.019689 -13534 -1420 -1602.0 -4133 1.0 1 1 0 1 0 0 NaN 3.0 2 2 MONDAY 9 0 0 0 0 1 1 Business Entity Type 3 NaN 0.633608 0.210350 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 0.0 0.0 0.0 0.0 0.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 4.0
279 100322 0 Cash loans M Y Y 2 225000.000 254700.0 26874.0 225000.0 Spouse, partner Working Secondary / secondary special Married House / apartment 0.007330 -19297 -554 -7173.0 -2853 20.0 1 1 0 1 0 0 Laborers 4.0 2 2 MONDAY 13 0 1 1 0 1 1 Business Entity Type 3 NaN 0.731710 0.638044 0.0247 0.0480 0.9901 0.8640 NaN NaN 0.0690 0.0833 NaN NaN NaN 0.0274 NaN 0.0103 0.0252 0.0498 0.9901 0.8693 NaN NaN 0.0690 0.0833 NaN NaN NaN 0.0286 NaN 0.0109 0.0250 0.0480 0.9901 0.8658 NaN NaN 0.0690 0.0833 NaN NaN NaN 0.0279 NaN 0.0105 NaN block of flats 0.0238 Monolithic No 0.0 0.0 0.0 0.0 -1644.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 0.0
280 100323 0 Cash loans M Y Y 0 450000.000 1575000.0 55800.0 1575000.0 Unaccompanied Commercial associate Higher education Married House / apartment 0.026392 -18213 -2363 -4627.0 -1761 3.0 1 1 1 1 1 0 Managers 2.0 2 2 WEDNESDAY 11 0 0 0 0 0 0 Business Entity Type 3 NaN 0.663695 NaN 0.1227 0.1214 0.9786 NaN NaN 0.0000 0.2759 0.1667 NaN 0.0940 NaN 0.1059 NaN 0.0000 0.1250 0.1260 0.9786 NaN NaN 0.0000 0.2759 0.1667 NaN 0.0961 NaN 0.1103 NaN 0.0000 0.1239 0.1214 0.9786 NaN NaN 0.00 0.2759 0.1667 NaN 0.0956 NaN 0.1078 NaN 0.0000 NaN block of flats 0.0833 Panel No 6.0 0.0 6.0 0.0 -206.0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 NaN NaN NaN NaN NaN NaN
281 100324 0 Cash loans F Y Y 1 112500.000 152820.0 18265.5 135000.0 Unaccompanied Commercial associate Secondary / secondary special Separated House / apartment 0.003069 -10471 -1753 -4922.0 -2885 1.0 1 1 0 1 0 0 Sales staff 2.0 3 3 THURSDAY 17 0 0 0 0 0 0 Self-employed 0.470460 0.659141 NaN 0.1649 0.0928 0.9901 NaN NaN 0.1200 0.1034 0.3750 NaN 0.0145 NaN 0.0770 NaN 0.0000 0.1681 0.0963 0.9901 NaN NaN 0.1208 0.1034 0.3750 NaN 0.0149 NaN 0.0803 NaN 0.0000 0.1665 0.0928 0.9901 NaN NaN 0.12 0.1034 0.3750 NaN 0.0148 NaN 0.0784 NaN 0.0000 NaN block of flats 0.1047 Panel No 0.0 0.0 0.0 0.0 -1957.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NaN NaN NaN NaN NaN NaN
282 100325 0 Cash loans F N Y 0 135000.000 640080.0 31261.5 450000.0 Unaccompanied State servant Secondary / secondary special Married House / apartment 0.031329 -20385 -2875 -4025.0 -3617 NaN 1 1 0 1 0 0 Laborers 2.0 2 2 TUESDAY 9 0 0 0 0 1 1 Construction NaN 0.588408 0.549597 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 0.0 0.0 0.0 0.0 -1474.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 1.0
283 100326 1 Cash loans M Y Y 0 36000.000 284400.0 10849.5 225000.0 Unaccompanied Pensioner Secondary / secondary special Civil marriage House / apartment 0.018209 -22078 365243 -6748.0 -4542 27.0 1 0 0 1 1 0 NaN 2.0 3 3 MONDAY 7 0 0 0 0 0 0 XNA NaN 0.063455 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 0.0 0.0 0.0 0.0 0.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 0.0
284 100327 0 Cash loans M Y Y 0 180000.000 407727.0 32341.5 333000.0 Family Working Secondary / secondary special Married House / apartment 0.020713 -20317 -2334 -10946.0 -3680 3.0 1 1 0 1 0 0 NaN 2.0 3 3 TUESDAY 11 0 0 0 0 1 1 Business Entity Type 3 NaN 0.395245 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 0.0 0.0 0.0 0.0 -1858.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 9.0
285 100328 0 Cash loans F N N 1 171000.000 1270746.0 42124.5 1138500.0 Unaccompanied State servant Secondary / secondary special Married House / apartment 0.004960 -15864 -8215 -2463.0 -2451 NaN 1 1 0 1 0 0 Core staff 3.0 2 2 TUESDAY 10 0 0 0 0 0 0 Medicine 0.616187 0.692311 0.735221 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 1.0 1.0 1.0 1.0 -715.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 2.0
286 100329 0 Cash loans F Y Y 0 135000.000 247275.0 17716.5 225000.0 Unaccompanied Working Secondary / secondary special Married House / apartment 0.006852 -19997 -645 -2391.0 -3399 19.0 1 1 1 1 1 0 NaN 2.0 3 3 FRIDAY 8 0 1 1 0 1 1 Business Entity Type 3 NaN 0.081311 0.629674 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 1.0 0.0 1.0 0.0 -1477.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 1.0
287 100332 0 Cash loans F N Y 0 157500.000 545040.0 20677.5 450000.0 Family Pensioner Secondary / secondary special Widow House / apartment 0.031329 -23444 365243 -14604.0 -4655 NaN 1 0 0 1 0 0 NaN 1.0 2 2 THURSDAY 13 0 0 0 0 0 0 XNA NaN 0.119843 0.331251 0.1485 0.0757 0.9801 NaN NaN 0.1600 0.1379 0.3333 NaN 0.0858 NaN 0.1443 NaN 0.0000 0.1513 0.0786 0.9801 NaN NaN 0.1611 0.1379 0.3333 NaN 0.0877 NaN 0.1504 NaN 0.0000 0.1499 0.0757 0.9801 NaN NaN 0.16 0.1379 0.3333 NaN 0.0873 NaN 0.1469 NaN 0.0000 NaN block of flats 0.1214 Panel No 0.0 0.0 0.0 0.0 0.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 1.0 0.0 0.0
288 100333 0 Cash loans F N Y 0 382500.000 450000.0 30204.0 450000.0 Family Commercial associate Incomplete higher Married House / apartment 0.072508 -10442 -864 -4750.0 -243 NaN 1 1 0 1 1 1 Core staff 2.0 1 1 TUESDAY 18 0 0 0 0 0 0 Bank 0.503806 0.588594 0.309275 0.1237 0.0925 0.9737 0.6396 0.0681 0.0000 0.2069 0.1667 0.1042 0.0079 0.1009 0.0906 0.0000 0.0020 0.0630 0.0646 0.9732 0.6472 0.0237 0.0000 0.1034 0.1667 0.0000 0.0000 0.0551 0.0360 0.0000 0.0000 0.1249 0.0925 0.9737 0.6444 0.0686 0.00 0.2069 0.1667 0.1042 0.0080 0.1026 0.0922 0.0000 0.0021 reg oper account block of flats 0.0409 Stone, brick No 10.0 0.0 10.0 0.0 -984.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 1.0
289 100334 0 Cash loans M N Y 0 90000.000 101880.0 10939.5 90000.0 Unaccompanied Working Secondary / secondary special Married House / apartment 0.035792 -18665 -837 -6710.0 -2210 NaN 1 1 0 1 0 0 Security staff 2.0 2 2 SATURDAY 12 0 0 0 0 0 0 Other NaN 0.703632 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 0.0 0.0 0.0 0.0 -373.0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 1.0
290 100335 0 Cash loans F N Y 0 63000.000 675000.0 19737.0 675000.0 Unaccompanied Pensioner Secondary / secondary special Widow House / apartment 0.035792 -23420 365243 -14293.0 -4749 NaN 1 0 0 1 1 0 NaN 1.0 2 2 MONDAY 8 0 0 0 0 0 0 XNA NaN 0.703773 0.735221 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 0.0 0.0 0.0 0.0 -1331.0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 0.0
291 100336 1 Cash loans F Y Y 0 157500.000 497520.0 28692.0 450000.0 Unaccompanied Commercial associate Secondary / secondary special Single / not married House / apartment 0.024610 -13989 -1350 -8112.0 -4715 5.0 1 1 1 1 1 0 Laborers 1.0 2 2 MONDAY 15 0 0 0 0 0 0 Industry: type 3 NaN 0.629042 0.260856 0.1485 0.1110 0.9801 0.7280 0.0725 0.1600 0.1379 0.3333 0.3750 0.0582 0.1210 0.1514 0.0000 0.0000 0.1513 0.1152 0.9801 0.7387 0.0732 0.1611 0.1379 0.3333 0.3750 0.0595 0.1322 0.1578 0.0000 0.0000 0.1499 0.1110 0.9801 0.7316 0.0730 0.16 0.1379 0.3333 0.3750 0.0592 0.1231 0.1541 0.0000 0.0000 reg oper account block of flats 0.1588 Panel No 1.0 1.0 1.0 0.0 -1781.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 1.0 1.0
292 100337 0 Cash loans F N Y 0 270000.000 545040.0 26640.0 450000.0 Unaccompanied Commercial associate Higher education Single / not married House / apartment 0.046220 -9324 -872 -599.0 -616 NaN 1 1 0 1 0 0 Realty agents 1.0 1 1 TUESDAY 15 0 1 1 0 0 0 Business Entity Type 3 NaN 0.712186 NaN 0.0722 0.0575 0.9776 0.6940 0.0258 0.0000 0.1379 0.1667 0.2083 0.0387 0.0588 0.0569 0.0000 0.0000 0.0735 0.0596 0.9777 0.7060 0.0260 0.0000 0.1379 0.1667 0.2083 0.0395 0.0643 0.0593 0.0000 0.0000 0.0729 0.0575 0.9776 0.6981 0.0260 0.00 0.1379 0.1667 0.2083 0.0393 0.0599 0.0579 0.0000 0.0000 reg oper account block of flats 0.0589 Stone, brick No 0.0 0.0 0.0 0.0 0.0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NaN NaN NaN NaN NaN NaN
293 100338 0 Cash loans F N N 0 270000.000 630000.0 23274.0 630000.0 Family Working Secondary / secondary special Civil marriage House / apartment 0.008019 -21292 -1491 -11804.0 -4059 NaN 1 1 1 1 1 1 High skill tech staff 2.0 2 2 SATURDAY 16 0 1 1 0 1 1 Trade: type 6 0.786704 0.754831 0.470456 0.0825 0.0677 0.9762 0.6736 0.0096 0.0000 0.1379 0.1667 0.2083 0.0472 0.0672 0.0708 0.0000 0.0000 0.0840 0.0703 0.9762 0.6864 0.0097 0.0000 0.1379 0.1667 0.2083 0.0483 0.0735 0.0737 0.0000 0.0000 0.0833 0.0677 0.9762 0.6780 0.0096 0.00 0.1379 0.1667 0.2083 0.0480 0.0684 0.0721 0.0000 0.0000 reg oper account block of flats 0.0609 Panel No 0.0 0.0 0.0 0.0 -115.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 3.0
294 100339 0 Revolving loans M N Y 0 166500.000 495000.0 24750.0 495000.0 Unaccompanied Commercial associate Secondary / secondary special Married House / apartment 0.011703 -21535 -2828 -3182.0 -4673 NaN 1 1 0 1 0 0 Laborers 2.0 2 2 MONDAY 17 0 0 0 0 0 0 Business Entity Type 3 NaN 0.766615 0.778904 0.0639 0.0450 0.9732 0.6328 0.0052 0.0000 0.1034 0.1667 0.0417 0.0350 0.0504 0.0484 0.0077 0.0000 0.0651 0.0467 0.9732 0.6472 0.0053 0.0000 0.1034 0.1667 0.0417 0.0358 0.0551 0.0505 0.0078 0.0000 0.0645 0.0450 0.9732 0.6377 0.0053 0.00 0.1034 0.1667 0.0417 0.0356 0.0513 0.0493 0.0078 0.0000 reg oper account block of flats 0.0410 Stone, brick No 0.0 0.0 0.0 0.0 -1560.0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 5.0
295 100340 0 Cash loans F N N 2 202500.000 787131.0 44082.0 679500.0 Unaccompanied Commercial associate Secondary / secondary special Married House / apartment 0.046220 -13080 -3429 -4198.0 -5241 NaN 1 1 0 1 0 0 High skill tech staff 4.0 1 1 SUNDAY 12 0 0 0 0 1 1 Transport: type 4 0.402436 0.261967 NaN NaN NaN 0.9727 NaN NaN NaN 0.0345 0.0417 NaN NaN NaN NaN NaN NaN NaN NaN 0.9727 NaN NaN NaN 0.0345 0.0417 NaN NaN NaN NaN NaN NaN NaN NaN 0.9727 NaN NaN NaN 0.0345 0.0417 NaN NaN NaN NaN NaN NaN NaN block of flats 0.0066 Stone, brick No 3.0 0.0 3.0 0.0 -2218.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NaN NaN NaN NaN NaN NaN
296 100341 0 Cash loans M Y Y 0 76500.000 545040.0 20677.5 450000.0 Unaccompanied Working Secondary / secondary special Widow House / apartment 0.031329 -20348 -7115 -1799.0 -2780 28.0 1 1 0 1 0 0 Laborers 1.0 2 2 TUESDAY 10 0 0 0 0 0 0 Industry: type 2 0.660390 0.647373 0.315472 0.0485 0.0328 0.9816 0.7484 0.0216 0.0000 0.0345 0.1667 0.2083 0.0120 0.0395 0.0218 0.0000 0.0000 0.0494 0.0340 0.9816 0.7583 0.0218 0.0000 0.0345 0.1667 0.2083 0.0122 0.0432 0.0228 0.0000 0.0000 0.0489 0.0328 0.9816 0.7518 0.0217 0.00 0.0345 0.1667 0.2083 0.0122 0.0402 0.0222 0.0000 0.0000 reg oper account block of flats 0.0410 Stone, brick No 0.0 0.0 0.0 0.0 0.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 1.0
297 100342 0 Cash loans M N N 0 90000.000 101880.0 10827.0 90000.0 Unaccompanied Working Secondary / secondary special Single / not married With parents 0.008866 -8400 -338 -4664.0 -1078 NaN 1 1 1 1 1 1 Laborers 1.0 2 2 THURSDAY 15 0 0 0 0 0 0 Business Entity Type 3 0.068260 0.031275 0.173527 0.2258 NaN 0.9911 0.8776 0.0355 0.0000 0.5172 0.1667 0.2083 0.0000 0.1841 0.2041 0.0000 0.0000 0.2300 NaN 0.9911 0.8824 0.0358 0.0000 0.5172 0.1667 0.2083 0.0000 0.2011 0.2127 0.0000 0.0000 0.2280 NaN 0.9911 0.8792 0.0357 0.00 0.5172 0.1667 0.2083 0.0000 0.1873 0.2078 0.0000 0.0000 reg oper account block of flats 0.1605 Mixed No 0.0 0.0 0.0 0.0 0.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 1.0
298 100343 0 Cash loans M Y Y 0 315000.000 90000.0 4504.5 90000.0 Unaccompanied Working Secondary / secondary special Single / not married With parents 0.022800 -10935 -422 -5251.0 -3364 2.0 1 1 0 1 1 0 Drivers 1.0 2 2 SATURDAY 11 0 0 0 0 0 0 Business Entity Type 3 0.259823 0.581980 0.537070 0.2959 0.1433 0.9871 0.8232 0.0706 0.3200 0.2759 0.3333 0.3750 0.1440 0.2404 0.3213 0.0039 0.0086 0.3015 0.1487 0.9871 0.8301 0.0712 0.3222 0.2759 0.3333 0.3750 0.1473 0.2626 0.3348 0.0039 0.0091 0.2987 0.1433 0.9871 0.8256 0.0710 0.32 0.2759 0.3333 0.3750 0.1465 0.2446 0.3271 0.0039 0.0087 org spec account block of flats 0.2932 Panel No 2.0 0.0 2.0 0.0 -1016.0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 1.0
299 100344 0 Cash loans M N N 0 112500.000 225000.0 14647.5 225000.0 Family Commercial associate Higher education Married House / apartment 0.032561 -11287 -3894 -3134.0 -3950 NaN 1 1 0 1 1 0 Laborers 2.0 1 1 FRIDAY 14 0 0 0 0 0 0 Business Entity Type 3 0.275940 0.642897 0.479449 0.2309 0.1795 0.9747 0.6532 0.0280 0.3200 0.2759 0.2500 0.0417 0.0536 0.1883 0.2008 0.0000 0.0000 0.2353 0.1862 0.9747 0.6668 0.0282 0.3222 0.2759 0.2500 0.0417 0.0548 0.2057 0.2092 0.0000 0.0000 0.2332 0.1795 0.9747 0.6578 0.0282 0.32 0.2759 0.2500 0.0417 0.0545 0.1915 0.2044 0.0000 0.0000 reg oper account block of flats 0.1601 Panel No 0.0 0.0 0.0 0.0 -1499.0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 1.0 0.0 3.0
300 100345 0 Cash loans F N N 0 157500.000 450000.0 17095.5 450000.0 Unaccompanied Working Secondary / secondary special Married House / apartment 0.019101 -21154 -951 -5892.0 -3720 NaN 1 1 1 1 0 0 Laborers 2.0 2 2 SATURDAY 13 0 0 0 0 0 0 Self-employed NaN 0.521550 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 3.0 0.0 3.0 0.0 -631.0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 3.0
301 100346 0 Cash loans F N Y 0 180000.000 582804.0 25803.0 463500.0 Unaccompanied State servant Higher education Separated House / apartment 0.024610 -17674 -6788 -10598.0 -1205 NaN 1 1 1 1 1 0 Core staff 1.0 2 2 FRIDAY 9 0 0 0 0 0 0 School 0.767531 0.617738 0.517297 0.0082 0.0000 0.9702 0.5920 0.0008 0.0000 0.0345 0.0417 0.0833 NaN 0.0067 0.0065 0.0000 0.0000 0.0084 0.0000 0.9702 0.6080 0.0008 0.0000 0.0345 0.0417 0.0833 NaN 0.0073 0.0067 0.0000 0.0000 0.0083 0.0000 0.9702 0.5975 0.0008 0.00 0.0345 0.0417 0.0833 NaN 0.0068 0.0066 0.0000 0.0000 reg oper account block of flats 0.0055 Wooden No 0.0 0.0 0.0 0.0 -1878.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 5.0
302 100347 0 Cash loans F N N 0 81000.000 640080.0 23121.0 450000.0 Unaccompanied Pensioner Secondary / secondary special Married House / apartment 0.018801 -21419 365243 -13511.0 -4287 NaN 1 0 0 1 0 0 NaN 2.0 2 2 SATURDAY 7 0 0 0 0 0 0 XNA NaN 0.531341 0.713631 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 0.0 0.0 0.0 0.0 -494.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 3.0
303 100348 0 Cash loans F N N 0 135000.000 1223010.0 47983.5 1125000.0 Unaccompanied Working Incomplete higher Married House / apartment 0.032561 -10390 -370 -8697.0 -1864 NaN 1 1 0 1 0 0 NaN 2.0 1 1 FRIDAY 15 0 0 0 0 0 0 Business Entity Type 3 NaN 0.598658 0.672243 0.2701 0.1641 0.9881 0.8368 0.0491 0.2400 0.2069 0.3750 0.4167 0.0000 0.2093 0.2736 0.0502 0.0086 0.2752 0.1703 0.9881 0.8432 0.0495 0.2417 0.2069 0.3750 0.4167 0.0000 0.2287 0.2850 0.0506 0.0092 0.2727 0.1641 0.9881 0.8390 0.0494 0.24 0.2069 0.3750 0.4167 0.0000 0.2129 0.2785 0.0505 0.0088 reg oper account block of flats 0.2170 Panel No 0.0 0.0 0.0 0.0 -6.0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 0.0
304 100349 0 Cash loans F N N 0 108000.000 364896.0 23449.5 315000.0 Unaccompanied Pensioner Secondary / secondary special Widow Municipal apartment 0.009334 -23338 365243 -11761.0 -4820 NaN 1 0 0 1 1 0 NaN 1.0 2 2 FRIDAY 14 0 0 0 0 0 0 XNA NaN 0.672712 0.297087 NaN 0.0000 0.9727 0.6260 0.0062 0.0000 0.1379 0.1250 0.1667 0.0213 0.0538 0.0514 0.0000 0.0000 NaN 0.0000 0.9727 0.6406 0.0062 0.0000 0.1379 0.1250 0.1667 0.0218 0.0588 0.0536 0.0000 0.0000 NaN 0.0000 0.9727 0.6310 0.0062 0.00 0.1379 0.1250 0.1667 0.0216 0.0547 0.0523 0.0000 0.0000 reg oper account block of flats 0.0438 Stone, brick No 0.0 0.0 0.0 0.0 -669.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 1.0 1.0
305 100350 0 Cash loans F N Y 0 112500.000 481495.5 31833.0 454500.0 Unaccompanied Pensioner Secondary / secondary special Married House / apartment 0.010500 -22759 365243 -124.0 -2596 NaN 1 0 0 1 1 0 NaN 2.0 3 3 MONDAY 12 0 0 0 0 0 0 XNA NaN 0.501308 0.600658 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 0.0 0.0 0.0 0.0 -1711.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 4.0
306 100351 0 Cash loans F N N 0 69750.000 675000.0 20596.5 675000.0 Unaccompanied Pensioner Secondary / secondary special Married House / apartment 0.014520 -22606 365243 -930.0 -4475 NaN 1 0 0 1 0 0 NaN 2.0 2 2 TUESDAY 12 0 0 0 0 0 0 XNA NaN 0.666442 0.456110 0.0959 0.0760 0.9851 NaN NaN 0.0000 0.1379 0.1667 NaN 0.0355 NaN 0.0667 NaN 0.0268 0.0977 0.0788 0.9851 NaN NaN 0.0000 0.1379 0.1667 NaN 0.0363 NaN 0.0695 NaN 0.0284 0.0968 0.0760 0.9851 NaN NaN 0.00 0.1379 0.1667 NaN 0.0361 NaN 0.0679 NaN 0.0274 NaN block of flats 0.0583 Panel No 2.0 0.0 2.0 0.0 -957.0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 1.0 1.0
307 100353 0 Cash loans F N Y 0 81000.000 536917.5 19282.5 463500.0 Unaccompanied Working Secondary / secondary special Widow House / apartment 0.031329 -17941 -535 -13472.0 -1493 NaN 1 1 0 1 0 0 Sales staff 1.0 2 2 SATURDAY 8 0 0 0 0 0 0 Self-employed NaN 0.167103 NaN 0.1237 0.1069 0.9816 0.7484 0.0169 0.0000 0.1379 0.1667 0.2083 0.1279 0.1009 0.1106 0.0000 0.0444 0.1261 0.1109 0.9816 0.7583 0.0170 0.0000 0.1379 0.1667 0.2083 0.1309 0.1102 0.1153 0.0000 0.0470 0.1249 0.1069 0.9816 0.7518 0.0170 0.00 0.1379 0.1667 0.2083 0.1302 0.1026 0.1126 0.0000 0.0453 reg oper account block of flats 0.0962 Stone, brick No 6.0 0.0 5.0 0.0 -617.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NaN NaN NaN NaN NaN NaN
308 100354 0 Revolving loans F N Y 1 112500.000 135000.0 6750.0 135000.0 Unaccompanied Commercial associate Secondary / secondary special Single / not married House / apartment 0.018209 -9259 -2382 -3948.0 -1918 NaN 1 1 1 1 1 0 Sales staff 2.0 3 3 THURSDAY 11 0 0 0 0 0 0 Business Entity Type 3 NaN 0.042010 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 5.0 0.0 5.0 0.0 -1135.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NaN NaN NaN NaN NaN NaN
309 100355 0 Cash loans M N Y 0 315000.000 946503.0 42898.5 846000.0 Unaccompanied Working Secondary / secondary special Married With parents 0.003813 -9178 -633 -4829.0 -1622 NaN 1 1 0 1 0 0 NaN 2.0 2 2 MONDAY 10 0 1 1 0 1 1 Business Entity Type 2 NaN 0.184220 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 0.0 0.0 0.0 0.0 -223.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NaN NaN NaN NaN NaN NaN
310 100356 0 Cash loans F N Y 0 180000.000 188685.0 14944.5 157500.0 Unaccompanied Working Secondary / secondary special Widow House / apartment 0.006305 -21140 -385 -4410.0 -2820 NaN 1 1 0 1 0 0 Cleaning staff 1.0 3 3 THURSDAY 3 0 0 0 0 0 0 Business Entity Type 3 NaN 0.561955 0.544235 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 0.0 0.0 0.0 0.0 -583.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 2.0
311 100357 0 Revolving loans F N Y 0 225000.000 180000.0 9000.0 180000.0 Unaccompanied Commercial associate Incomplete higher Civil marriage House / apartment 0.046220 -9166 -1450 -9166.0 -547 NaN 1 1 0 1 0 1 Accountants 2.0 1 1 SATURDAY 16 0 0 0 0 1 1 Business Entity Type 3 NaN 0.765106 0.379100 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 0.0 0.0 0.0 0.0 -653.0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 0.0
312 100359 0 Cash loans M Y Y 1 112500.000 226908.0 11164.5 148500.0 Unaccompanied Working Secondary / secondary special Married House / apartment 0.025164 -14923 -273 -3883.0 -4003 12.0 1 1 0 1 0 0 Laborers 3.0 2 2 WEDNESDAY 9 0 1 1 0 1 1 Business Entity Type 3 NaN 0.573841 0.799297 0.0722 0.0846 0.9821 0.7552 0.0070 0.0000 0.1379 0.1667 0.2083 0.0000 0.0588 0.0674 0.0000 0.0000 0.0735 0.0878 0.9821 0.7648 0.0071 0.0000 0.1379 0.1667 0.2083 0.0000 0.0643 0.0702 0.0000 0.0000 0.0729 0.0846 0.9821 0.7585 0.0071 0.00 0.1379 0.1667 0.2083 0.0000 0.0599 0.0686 0.0000 0.0000 reg oper account block of flats 0.0568 Stone, brick No 4.0 1.0 4.0 0.0 -743.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 1.0
313 100360 0 Revolving loans F N N 0 67500.000 202500.0 10125.0 202500.0 Unaccompanied Commercial associate Secondary / secondary special Married House / apartment 0.025164 -19676 -4483 -5545.0 -3236 NaN 1 1 0 1 0 0 NaN 2.0 2 2 THURSDAY 8 0 0 0 0 0 0 Housing 0.861626 0.756108 NaN 0.1220 0.1097 0.9771 0.6872 0.0720 0.0000 0.2528 0.1667 0.2083 0.0525 0.1084 0.0876 0.0039 0.0128 0.1008 0.0981 0.9772 0.6994 0.0134 0.0000 0.2759 0.1667 0.2083 0.0221 0.1185 0.0705 0.0039 0.0019 0.1343 0.1097 0.9771 0.6914 0.0482 0.00 0.2759 0.1667 0.2083 0.0534 0.1103 0.0894 0.0039 0.0025 org spec account block of flats 0.0766 Block No 0.0 0.0 0.0 0.0 -1232.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 1.0 0.0 0.0 2.0
314 100361 0 Cash loans M N Y 0 270000.000 1800000.0 62698.5 1800000.0 Unaccompanied Working Secondary / secondary special Married House / apartment 0.007274 -17099 -499 -5508.0 -649 NaN 1 1 0 1 0 0 Laborers 2.0 2 2 TUESDAY 11 0 0 0 0 0 0 Trade: type 7 0.404589 0.433919 0.786267 0.0680 0.0836 0.9896 0.8572 0.0178 0.0000 0.1724 0.1667 0.0417 NaN 0.0555 0.0878 0.0000 0.0000 0.0693 0.0868 0.9896 0.8628 0.0179 0.0000 0.1724 0.1667 0.0417 NaN 0.0606 0.0915 0.0000 0.0000 0.0687 0.0836 0.9896 0.8591 0.0179 0.00 0.1724 0.1667 0.0417 NaN 0.0564 0.0894 0.0000 0.0000 reg oper account block of flats 0.0690 Panel No 0.0 0.0 0.0 0.0 -991.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 4.0
315 100362 0 Cash loans F N Y 1 157500.000 807984.0 26833.5 697500.0 Unaccompanied Commercial associate Secondary / secondary special Civil marriage House / apartment 0.031329 -17438 -3668 -10185.0 -974 NaN 1 1 0 1 0 0 Sales staff 3.0 2 2 WEDNESDAY 16 0 0 0 0 0 0 Business Entity Type 2 NaN 0.640202 0.605836 0.0923 0.0835 0.9846 0.7620 0.0427 0.0800 0.0690 0.3333 0.3750 0.0612 0.0908 0.0719 0.0000 0.0537 0.0746 0.0867 0.9826 0.7713 0.0431 0.0403 0.0345 0.3333 0.3750 0.0581 0.0992 0.0267 0.0000 0.0000 0.0932 0.0835 0.9846 0.7652 0.0430 0.08 0.0690 0.3333 0.3750 0.0623 0.0923 0.0732 0.0000 0.0549 org spec account block of flats 0.0378 Panel No 1.0 0.0 1.0 0.0 -1698.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 1.0 4.0
316 100363 0 Cash loans F Y Y 1 360000.000 493497.0 48942.0 454500.0 Unaccompanied Commercial associate Higher education Married House / apartment 0.006629 -14882 -436 -1140.0 -4606 6.0 1 1 0 1 0 1 Core staff 3.0 2 2 MONDAY 11 0 0 0 0 0 0 Bank 0.735443 0.462205 0.540654 0.0784 0.0000 0.9950 0.9320 0.0194 0.0800 0.0690 0.2500 0.0417 0.0979 0.0630 0.1019 0.0618 0.0544 0.0798 0.0000 0.9950 0.9347 0.0195 0.0806 0.0690 0.2500 0.0417 0.1001 0.0689 0.1062 0.0623 0.0576 0.0791 0.0000 0.9950 0.9329 0.0195 0.08 0.0690 0.2500 0.0417 0.0996 0.0641 0.1037 0.0621 0.0556 reg oper spec account block of flats 0.0970 Block No 0.0 0.0 0.0 0.0 -563.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1.0 0.0 0.0 0.0 0.0 1.0
317 100364 0 Cash loans F N N 0 315000.000 1024290.0 30078.0 855000.0 Family Working Secondary / secondary special Married House / apartment 0.019101 -17498 -1355 -8464.0 -1050 NaN 1 1 0 1 0 0 Laborers 2.0 2 2 WEDNESDAY 16 0 0 0 0 1 1 Industry: type 11 NaN 0.331750 0.720944 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 3.0 1.0 3.0 0.0 0.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 1.0 0.0 0.0 4.0
318 100365 0 Cash loans F N Y 0 247500.000 517788.0 19647.0 427500.0 Unaccompanied Pensioner Higher education Married House / apartment 0.010032 -20435 365243 -2565.0 -3916 NaN 1 0 0 1 0 0 NaN 2.0 2 2 FRIDAY 4 0 0 0 0 0 0 XNA NaN 0.669967 0.263647 0.0715 0.0988 0.9742 0.6464 NaN 0.0000 0.1379 0.1667 NaN NaN NaN 0.0442 NaN NaN 0.0609 0.0660 0.9717 0.6276 NaN 0.0000 0.1034 0.1667 NaN NaN NaN 0.0346 NaN NaN 0.0666 0.0805 0.9752 0.6645 NaN 0.00 0.1379 0.1667 NaN NaN NaN 0.0341 NaN NaN reg oper account block of flats 0.0395 Stone, brick No 0.0 0.0 0.0 0.0 -358.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 1.0 1.0 4.0
319 100366 0 Revolving loans M Y Y 0 765000.000 1350000.0 67500.0 1350000.0 Unaccompanied Commercial associate Higher education Single / not married House / apartment 0.002506 -11788 -3144 -5643.0 -3962 3.0 1 1 0 1 0 0 Managers 1.0 2 2 THURSDAY 7 0 0 0 1 1 0 Business Entity Type 3 0.421765 0.765981 0.511892 0.0330 0.0374 0.9831 0.7688 0.0093 0.0000 0.0690 0.1250 0.1667 0.0087 0.0269 0.0327 0.0000 0.0252 0.0336 0.0388 0.9831 0.7779 0.0094 0.0000 0.0690 0.1250 0.1667 0.0089 0.0294 0.0341 0.0000 0.0266 0.0333 0.0374 0.9831 0.7719 0.0094 0.00 0.0690 0.1250 0.1667 0.0088 0.0274 0.0333 0.0000 0.0257 reg oper account NaN 0.0336 Block No 1.0 0.0 0.0 0.0 -151.0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 0.0
320 100368 0 Cash loans F N Y 0 166500.000 675000.0 24246.0 675000.0 Family Working Secondary / secondary special Married House / apartment 0.035792 -11285 -3473 -1929.0 -3094 NaN 1 1 1 1 1 0 NaN 2.0 2 2 WEDNESDAY 10 0 0 0 0 0 0 Business Entity Type 3 0.393172 0.689565 0.431192 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 4.0 0.0 4.0 0.0 -398.0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 5.0
321 100369 0 Cash loans F N Y 0 157500.000 528633.0 22527.0 472500.0 Unaccompanied Commercial associate Secondary / secondary special Married House / apartment 0.010500 -19323 -571 -67.0 -2779 NaN 1 1 0 1 0 0 Laborers 2.0 3 3 TUESDAY 16 0 0 0 0 0 0 Self-employed 0.719260 0.271443 0.771362 0.4082 0.1640 0.9995 0.9932 0.0683 0.3200 0.1379 0.6250 0.6667 0.1931 0.3291 0.2011 0.0174 0.1108 0.2437 0.0867 0.9995 0.9935 0.0582 0.1611 0.0690 0.6250 0.6667 0.1872 0.2048 0.1829 0.0000 0.0000 0.4122 0.1640 0.9995 0.9933 0.0687 0.32 0.1379 0.6250 0.6667 0.1965 0.3348 0.2047 0.0175 0.1131 reg oper account block of flats 0.2178 Block No 0.0 0.0 0.0 0.0 -459.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 2.0 1.0
322 100370 0 Cash loans F N Y 0 45000.000 448056.0 16222.5 315000.0 Children Pensioner Secondary / secondary special Married House / apartment 0.028663 -23195 365243 -9513.0 -4535 NaN 1 0 0 1 1 0 NaN 2.0 2 2 TUESDAY 12 0 0 0 0 0 0 XNA NaN 0.176041 0.595456 0.0727 0.0731 0.9876 0.8300 0.0113 0.0000 0.1724 0.1667 0.0417 0.0237 0.0580 0.0687 0.0058 0.0084 0.0473 0.0409 0.9871 0.8301 0.0070 0.0000 0.1034 0.1667 0.0417 0.0134 0.0413 0.0462 0.0000 0.0000 0.0734 0.0731 0.9876 0.8323 0.0114 0.00 0.1724 0.1667 0.0417 0.0241 0.0590 0.0699 0.0058 0.0086 reg oper account block of flats 0.0387 Stone, brick No 8.0 2.0 7.0 0.0 -1088.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 2.0 4.0
323 100371 0 Cash loans F Y Y 1 450000.000 808650.0 31464.0 675000.0 Unaccompanied Commercial associate Higher education Civil marriage House / apartment 0.072508 -14768 -426 -3241.0 -3409 1.0 1 1 0 1 1 1 Core staff 3.0 1 1 WEDNESDAY 13 1 1 0 0 0 0 Kindergarten 0.848803 0.615675 0.524496 0.4572 0.1461 0.9945 0.9252 0.0000 0.4400 0.1897 0.6667 0.0000 0.0000 0.3728 0.4587 0.0000 0.0534 0.1429 0.0534 0.9945 0.9281 0.0000 0.1611 0.0690 0.6667 0.0000 0.0000 0.1249 0.1794 0.0000 0.0051 0.4616 0.1461 0.9945 0.9262 0.0000 0.44 0.1897 0.6667 0.0000 0.0000 0.3792 0.4669 0.0000 0.0545 reg oper spec account block of flats 0.6093 Panel No 0.0 0.0 0.0 0.0 0.0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 0.0
324 100372 0 Cash loans F N N 0 90000.000 531000.0 29781.0 531000.0 Unaccompanied Pensioner Secondary / secondary special Married House / apartment 0.015221 -21909 365243 -2319.0 -4279 NaN 1 0 0 1 1 0 NaN 2.0 2 2 THURSDAY 11 0 0 0 0 0 0 XNA NaN 0.673737 0.465069 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 0.0 0.0 0.0 0.0 0.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 0.0
325 100373 0 Cash loans F N N 0 225000.000 906228.0 46269.0 810000.0 Unaccompanied State servant Secondary / secondary special Married House / apartment 0.030755 -15013 -3282 -2347.0 -4823 NaN 1 1 1 1 0 0 Core staff 2.0 2 2 FRIDAY 12 0 0 0 0 0 0 School 0.623531 0.625709 0.306202 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 0.0 0.0 0.0 0.0 -1053.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 0.0
326 100374 0 Cash loans F Y Y 2 112500.000 1308964.5 42354.0 1143000.0 Unaccompanied Working Secondary / secondary special Married House / apartment 0.003813 -14975 -7540 -7657.0 -4236 17.0 1 1 0 1 0 0 Laborers 4.0 2 2 WEDNESDAY 6 0 0 0 0 1 1 Transport: type 2 NaN 0.486442 0.544235 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 1.0 0.0 1.0 0.0 -644.0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 2.0
327 100375 0 Cash loans M N Y 0 180000.000 942300.0 30528.0 675000.0 Unaccompanied Commercial associate Higher education Married House / apartment 0.006305 -21552 -2672 -13057.0 -4188 NaN 1 1 0 1 1 0 NaN 2.0 3 3 WEDNESDAY 7 0 0 0 0 0 0 Transport: type 1 NaN 0.291624 0.377404 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 0.0 0.0 0.0 0.0 0.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 0.0
328 100376 0 Cash loans M Y Y 0 360000.000 254700.0 20250.0 225000.0 Unaccompanied Working Secondary / secondary special Married House / apartment 0.018801 -18831 -1342 -8691.0 -2067 17.0 1 1 0 1 0 0 Drivers 2.0 2 2 MONDAY 10 0 0 0 0 0 0 Transport: type 3 0.791412 0.445987 0.461482 0.4402 0.2371 0.9831 0.7688 0.1488 0.3200 0.2759 0.3333 0.3750 0.1312 0.3589 0.3408 0.0000 0.0000 0.4485 0.2460 0.9831 0.7779 0.1502 0.3222 0.2759 0.3333 0.3750 0.1342 0.3921 0.3551 0.0000 0.0000 0.4445 0.2371 0.9831 0.7719 0.1498 0.32 0.2759 0.3333 0.3750 0.1335 0.3651 0.3469 0.0000 0.0000 reg oper account block of flats 0.2680 Panel No 0.0 0.0 0.0 0.0 -542.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 1.0
329 100377 0 Cash loans M N N 2 157500.000 239850.0 25447.5 225000.0 Unaccompanied Working Higher education Married House / apartment 0.009549 -14252 -1281 -6024.0 -3427 NaN 1 1 1 1 0 0 High skill tech staff 4.0 2 2 TUESDAY 10 0 0 0 1 0 1 Business Entity Type 3 0.642073 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 1.0 0.0 1.0 0.0 -344.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NaN NaN NaN NaN NaN NaN
330 100378 0 Cash loans F Y Y 1 247500.000 808650.0 26217.0 675000.0 Unaccompanied Working Secondary / secondary special Widow House / apartment 0.008575 -15017 -639 -3996.0 -3979 3.0 1 1 0 1 0 0 Sales staff 2.0 2 2 THURSDAY 12 0 0 0 0 0 0 Self-employed NaN 0.528966 0.706205 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 1.0 0.0 1.0 0.0 -811.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 1.0 3.0
331 100379 0 Cash loans M Y Y 0 112500.000 942300.0 30528.0 675000.0 Family Working Secondary / secondary special Married House / apartment 0.009657 -19003 -5136 -5341.0 -2079 22.0 1 1 0 1 0 0 Drivers 2.0 2 2 MONDAY 11 0 0 0 0 1 1 Agriculture NaN 0.560086 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 0.0 0.0 0.0 0.0 -658.0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 NaN NaN NaN NaN NaN NaN
332 100380 0 Cash loans F N Y 0 630000.000 900000.0 57519.0 900000.0 Family Working Higher education Married House / apartment 0.072508 -15023 -1481 -3.0 -2483 NaN 1 1 0 1 0 0 High skill tech staff 2.0 1 1 SATURDAY 13 0 0 0 0 0 0 Business Entity Type 3 NaN 0.598937 NaN 0.0593 0.0650 0.9727 0.6260 0.0000 0.0000 0.1207 0.1667 0.2083 0.0000 0.0483 0.0411 0.0000 0.0717 0.0504 0.0634 0.9717 0.6276 0.0000 0.0000 0.1034 0.1667 0.2083 0.0000 0.0441 0.0389 0.0000 0.0512 0.0599 0.0650 0.9727 0.6310 0.0000 0.00 0.1207 0.1667 0.2083 0.0000 0.0492 0.0418 0.0000 0.0732 reg oper account block of flats 0.0664 Stone, brick No 0.0 0.0 0.0 0.0 0.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NaN NaN NaN NaN NaN NaN
333 100381 0 Cash loans F N N 0 121500.000 302544.0 11533.5 198000.0 Unaccompanied Commercial associate Higher education Single / not married With parents 0.018029 -11223 -107 -5619.0 -3755 NaN 1 1 0 1 0 0 NaN 1.0 3 3 SATURDAY 8 0 1 1 0 0 0 Business Entity Type 3 NaN 0.731130 NaN 0.1485 0.1263 0.9826 0.7620 0.0321 0.2000 0.1724 0.3333 0.0417 NaN NaN 0.1818 NaN 0.0000 0.1513 0.1310 0.9826 0.7713 0.0324 0.2014 0.1724 0.3333 0.0417 NaN NaN 0.1894 NaN 0.0000 0.1499 0.1263 0.9826 0.7652 0.0323 0.20 0.1724 0.3333 0.0417 NaN NaN 0.1850 NaN 0.0000 NaN block of flats 0.2016 Panel No 3.0 0.0 3.0 0.0 0.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 2.0
334 100383 0 Cash loans M Y N 2 180000.000 640080.0 20929.5 450000.0 Spouse, partner Working Secondary / secondary special Married House / apartment 0.019101 -13754 -819 -256.0 -416 22.0 1 1 0 1 0 0 Drivers 4.0 2 2 SUNDAY 15 0 0 0 0 1 1 Business Entity Type 3 NaN 0.324821 0.425893 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 13.0 0.0 13.0 0.0 -217.0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 1.0 9.0
335 100387 0 Cash loans M Y Y 2 292500.000 1095817.5 43587.0 1008000.0 Unaccompanied Commercial associate Secondary / secondary special Married House / apartment 0.003069 -13299 -1559 -395.0 -4249 8.0 1 1 0 1 0 0 Drivers 4.0 3 3 FRIDAY 14 0 0 0 0 0 0 Industry: type 3 0.209238 0.514818 0.551381 0.0629 0.0697 0.9796 NaN NaN 0.0000 0.1379 0.1667 NaN 0.0599 NaN 0.0361 NaN 0.0000 0.0641 0.0724 0.9796 NaN NaN 0.0000 0.1379 0.1667 NaN 0.0613 NaN 0.0376 NaN 0.0000 0.0635 0.0697 0.9796 NaN NaN 0.00 0.1379 0.1667 NaN 0.0610 NaN 0.0368 NaN 0.0000 NaN block of flats 0.0417 Stone, brick No 0.0 0.0 0.0 0.0 -757.0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 2.0 3.0
336 100388 0 Cash loans F N Y 0 99000.000 518562.0 20695.5 463500.0 Unaccompanied Working Secondary / secondary special Separated House / apartment 0.007020 -18929 -4145 -10610.0 -2482 NaN 1 1 0 1 0 0 Secretaries 1.0 2 2 WEDNESDAY 16 0 0 0 0 0 0 Housing 0.830737 0.343792 0.672243 0.0928 0.1057 0.9856 0.8028 0.0461 0.0000 0.2069 0.1667 NaN 0.0662 0.0756 0.0865 NaN NaN 0.0945 0.1097 0.9856 0.8105 0.0465 0.0000 0.2069 0.1667 NaN 0.0677 0.0826 0.0901 NaN NaN 0.0937 0.1057 0.9856 0.8054 0.0464 0.00 0.2069 0.1667 NaN 0.0673 0.0770 0.0881 NaN NaN reg oper account block of flats 0.0933 Stone, brick No 0.0 0.0 0.0 0.0 -55.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 2.0 0.0
337 100389 0 Cash loans M Y Y 0 247500.000 1971072.0 68643.0 1800000.0 Unaccompanied Working Higher education Married House / apartment 0.006207 -14216 -365 -3183.0 -4423 13.0 1 1 1 1 0 0 Core staff 2.0 2 2 WEDNESDAY 14 0 0 0 0 0 0 Self-employed 0.558582 0.748204 0.739412 0.2227 0.0355 0.9816 0.7484 0.0018 0.2400 0.2069 0.3333 0.3750 0.0877 0.1807 0.2295 0.0039 0.0906 0.2269 0.0368 0.9816 0.7583 0.0018 0.2417 0.2069 0.3333 0.3750 0.0897 0.1974 0.2392 0.0039 0.0959 0.2248 0.0355 0.9816 0.7518 0.0018 0.24 0.2069 0.3333 0.3750 0.0892 0.1838 0.2337 0.0039 0.0925 reg oper account block of flats 0.2002 Panel No 0.0 0.0 0.0 0.0 -2421.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 1.0
338 100390 0 Cash loans F N N 0 90000.000 555273.0 14778.0 463500.0 Family State servant Higher education Married House / apartment 0.007020 -11799 -1228 -334.0 -3678 NaN 1 1 0 1 0 0 High skill tech staff 2.0 2 2 FRIDAY 14 0 0 0 0 0 0 Transport: type 4 NaN 0.733214 0.657784 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 6.0 0.0 5.0 0.0 -3.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 2.0 0.0 0.0
339 100391 0 Cash loans F N Y 1 153000.000 808650.0 26217.0 675000.0 Family Working Secondary / secondary special Single / not married House / apartment 0.015221 -18315 -497 -2883.0 -1862 NaN 1 1 1 1 0 0 Sales staff 2.0 2 2 MONDAY 10 0 0 0 0 0 0 Other 0.635035 0.458153 0.832785 0.0237 0.0000 0.9762 0.6736 0.0013 0.0000 0.0690 0.0417 NaN 0.0282 0.0193 0.0137 0.0000 0.0000 0.0242 0.0000 0.9762 0.6864 0.0013 0.0000 0.0690 0.0417 NaN 0.0289 0.0211 0.0143 0.0000 0.0000 0.0239 0.0000 0.9762 0.6780 0.0013 0.00 0.0690 0.0417 NaN 0.0287 0.0197 0.0140 0.0000 0.0000 reg oper account block of flats 0.0115 Stone, brick No 0.0 0.0 0.0 0.0 -374.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 2.0
340 100392 0 Revolving loans M Y N 2 180000.000 180000.0 9000.0 180000.0 Spouse, partner Working Secondary / secondary special Civil marriage With parents 0.020246 -12325 -638 -560.0 -4029 17.0 1 1 0 1 0 0 Laborers 4.0 3 3 FRIDAY 7 0 0 0 0 0 0 Business Entity Type 3 0.097682 0.262656 0.729567 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 0.0 0.0 0.0 0.0 -215.0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 0.0
341 100393 0 Cash loans M Y Y 2 405000.000 2250000.0 59485.5 2250000.0 Unaccompanied Working Higher education Married House / apartment 0.007114 -14966 -6158 -954.0 -4979 6.0 1 1 0 1 1 0 Core staff 4.0 2 2 FRIDAY 13 0 0 0 0 0 0 Police NaN 0.682320 0.692559 0.1763 0.1337 0.9886 0.8436 NaN 0.2000 0.1724 0.3333 0.3750 0.0959 NaN 0.1918 NaN 0.0405 0.1796 0.1388 0.9886 0.8497 NaN 0.2014 0.1724 0.3333 0.3750 0.0980 NaN 0.1999 NaN 0.0429 0.1780 0.1337 0.9886 0.8457 NaN 0.20 0.1724 0.3333 0.3750 0.0975 NaN 0.1953 NaN 0.0413 NaN block of flats 0.1597 Panel No 0.0 0.0 0.0 0.0 -1014.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 1.0
342 100394 0 Cash loans M N Y 0 112500.000 178290.0 11034.0 157500.0 Unaccompanied Pensioner Secondary / secondary special Married House / apartment 0.035792 -23721 365243 -3367.0 -5013 NaN 1 0 0 1 0 0 NaN 2.0 2 2 WEDNESDAY 14 0 0 0 0 0 0 XNA NaN 0.657464 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 8.0 0.0 8.0 0.0 -1779.0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 4.0
343 100395 0 Cash loans M Y Y 0 225000.000 888840.0 32053.5 675000.0 Unaccompanied Commercial associate Secondary / secondary special Married Municipal apartment 0.072508 -16719 -687 -8892.0 -240 12.0 1 1 0 1 0 0 Drivers 2.0 1 1 FRIDAY 17 0 0 0 0 0 0 Business Entity Type 2 0.634742 0.775615 0.740799 0.0907 0.0701 0.9732 0.6328 0.0000 0.0000 0.1607 0.1667 0.0417 0.0000 0.0740 0.0673 0.0000 0.0180 0.0756 0.0457 0.9737 0.6537 0.0000 0.0000 0.1379 0.1667 0.0417 0.0000 0.0661 0.0677 0.0000 0.0000 0.0749 0.0829 0.9737 0.6444 0.0000 0.00 0.1379 0.1667 0.0417 0.0000 0.0616 0.0683 0.0000 0.0006 reg oper account block of flats 0.0511 Stone, brick No 0.0 0.0 0.0 0.0 -3351.0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 2.0
344 100396 1 Cash loans M N N 0 112500.000 417024.0 25330.5 360000.0 Family Pensioner Secondary / secondary special Single / not married House / apartment 0.025164 -13707 365243 -7809.0 -4436 NaN 1 0 0 1 0 0 NaN 1.0 2 2 SATURDAY 10 0 0 0 0 0 0 XNA NaN 0.293988 0.115387 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 0.0 0.0 0.0 0.0 0.0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 1.0
345 100397 0 Cash loans F N Y 0 144000.000 1417185.0 37512.0 1237500.0 Unaccompanied State servant Secondary / secondary special Married House / apartment 0.006233 -22767 -2998 -10042.0 -4410 NaN 1 1 1 1 1 0 Medicine staff 2.0 2 2 TUESDAY 9 0 0 0 0 0 0 Medicine NaN 0.790970 0.719491 0.0464 0.0030 0.9866 0.8164 0.0074 0.0000 0.1034 0.1667 0.2083 0.0259 0.0378 0.0415 0.0000 0.0024 0.0473 0.0032 0.9866 0.8236 0.0075 0.0000 0.1034 0.1667 0.2083 0.0264 0.0413 0.0432 0.0000 0.0025 0.0468 0.0030 0.9866 0.8189 0.0074 0.00 0.1034 0.1667 0.2083 0.0263 0.0385 0.0422 0.0000 0.0025 reg oper account block of flats 0.0332 Panel No 1.0 1.0 1.0 1.0 -2552.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 2.0
346 100398 0 Cash loans F N Y 0 81000.000 180000.0 8658.0 180000.0 Unaccompanied Working Secondary / secondary special Civil marriage House / apartment 0.030755 -20010 -2784 -4662.0 -3196 NaN 1 1 1 1 0 0 Cleaning staff 2.0 2 2 FRIDAY 14 0 0 0 0 0 0 Police 0.574438 0.545454 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 9.0 1.0 9.0 1.0 -1293.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NaN NaN NaN NaN NaN NaN
347 100400 0 Cash loans F N N 0 270000.000 536917.5 28467.0 463500.0 Unaccompanied Pensioner Secondary / secondary special Married House / apartment 0.020246 -23187 365243 -13629.0 -4573 NaN 1 0 0 1 0 0 NaN 2.0 3 3 THURSDAY 11 0 0 0 0 0 0 XNA NaN 0.442295 0.802745 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 0.0 0.0 0.0 0.0 -1553.0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 1.0 1.0
348 100401 1 Cash loans F N Y 0 202500.000 343683.0 16663.5 261000.0 Unaccompanied Pensioner Secondary / secondary special Separated House / apartment 0.010966 -20200 365243 -973.0 -2779 NaN 1 0 0 1 0 0 NaN 1.0 2 2 THURSDAY 10 0 0 0 0 0 0 XNA NaN 0.622575 NaN 0.1495 0.0813 0.9836 0.7756 0.0180 0.0400 0.0345 0.3333 0.3750 0.1030 0.1210 0.0933 0.0039 0.0256 0.1523 0.0843 0.9836 0.7844 0.0182 0.0403 0.0345 0.3333 0.3750 0.1054 0.1322 0.0972 0.0039 0.0271 0.1509 0.0813 0.9836 0.7786 0.0181 0.04 0.0345 0.3333 0.3750 0.1048 0.1231 0.0950 0.0039 0.0262 reg oper account block of flats 0.0888 Stone, brick No 0.0 0.0 0.0 0.0 0.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NaN NaN NaN NaN NaN NaN
349 100402 0 Cash loans F N Y 0 81000.000 276277.5 21825.0 238500.0 Unaccompanied Pensioner Secondary / secondary special Single / not married Municipal apartment 0.005144 -19780 365243 -11248.0 -3331 NaN 1 0 0 1 0 0 NaN 1.0 2 2 MONDAY 15 0 0 0 0 0 0 XNA NaN NaN NaN 0.0495 0.0413 0.9692 NaN NaN 0.0000 0.0690 0.0833 NaN 0.0215 NaN 0.0186 NaN 0.0000 0.0504 0.0429 0.9692 NaN NaN 0.0000 0.0690 0.0833 NaN 0.0220 NaN 0.0194 NaN 0.0000 0.0500 0.0413 0.9692 NaN NaN 0.00 0.0690 0.0833 NaN 0.0219 NaN 0.0190 NaN 0.0000 NaN specific housing 0.0213 Stone, brick No 1.0 0.0 1.0 0.0 -275.0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NaN NaN NaN NaN NaN NaN
350 100403 0 Cash loans M Y Y 0 315000.000 1350000.0 39604.5 1350000.0 Unaccompanied Working Higher education Married House / apartment 0.008575 -10615 -1496 -2119.0 -587 1.0 1 1 0 1 0 0 Sales staff 2.0 2 2 WEDNESDAY 14 0 0 0 1 1 0 Business Entity Type 3 0.137062 0.324174 0.486653 0.0742 0.0853 0.9871 0.8232 0.0107 0.0000 0.1724 0.1667 0.2083 NaN 0.0605 0.0702 0.0000 0.0214 0.0756 0.0885 0.9871 0.8301 0.0108 0.0000 0.1724 0.1667 0.2083 NaN 0.0661 0.0731 0.0000 0.0227 0.0749 0.0853 0.9871 0.8256 0.0108 0.00 0.1724 0.1667 0.2083 NaN 0.0616 0.0714 0.0000 0.0219 reg oper account block of flats 0.0657 Stone, brick No 1.0 0.0 1.0 0.0 -527.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 1.0 0.0 1.0
351 100405 0 Cash loans M Y Y 0 180000.000 450000.0 22018.5 450000.0 Unaccompanied Working Secondary / secondary special Single / not married House / apartment 0.010643 -8598 -220 -1058.0 -1282 12.0 1 1 1 1 1 0 Core staff 1.0 2 2 TUESDAY 10 0 1 1 0 1 1 Police 0.199625 0.381817 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 0.0 0.0 0.0 0.0 -963.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NaN NaN NaN NaN NaN NaN
352 100406 0 Cash loans F N Y 0 135000.000 468648.0 34101.0 414000.0 Unaccompanied Pensioner Secondary / secondary special Married House / apartment 0.010006 -20846 365243 -774.0 -4153 NaN 1 0 0 1 1 0 NaN 2.0 2 2 FRIDAY 8 0 0 0 0 0 0 XNA NaN 0.160741 0.713631 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 6.0 0.0 6.0 0.0 -164.0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 3.0
353 100407 0 Cash loans F N N 0 157500.000 521280.0 26743.5 450000.0 Family Working Higher education Married House / apartment 0.031329 -11582 -3632 -5277.0 -1294 NaN 1 1 1 1 1 0 Security staff 2.0 2 2 THURSDAY 13 0 0 0 0 0 0 Government 0.431068 0.540298 0.495666 0.1031 0.1392 0.9896 NaN NaN 0.0000 0.2759 0.1667 NaN NaN 0.0841 0.1142 0.0000 0.0000 0.1050 0.1445 0.9896 NaN NaN 0.0000 0.2759 0.1667 NaN NaN 0.0918 0.1190 0.0000 0.0000 0.1041 0.1392 0.9896 NaN NaN 0.00 0.2759 0.1667 NaN NaN 0.0855 0.1163 0.0000 0.0000 reg oper account block of flats 0.1002 Panel No 0.0 0.0 0.0 0.0 -1129.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 2.0
354 100408 0 Cash loans F N Y 0 81000.000 454500.0 29173.5 454500.0 Unaccompanied Commercial associate Incomplete higher Separated House / apartment 0.026392 -15781 -422 -3929.0 -4139 NaN 1 1 0 1 1 0 Cleaning staff 1.0 2 2 FRIDAY 10 0 0 0 0 1 1 Business Entity Type 3 NaN 0.435251 NaN 0.1485 NaN 0.9861 NaN NaN 0.1600 0.1379 0.3333 NaN NaN NaN 0.1529 NaN 0.0000 0.1513 NaN 0.9861 NaN NaN 0.1611 0.1379 0.3333 NaN NaN NaN 0.1593 NaN 0.0000 0.1499 NaN 0.9861 NaN NaN 0.16 0.1379 0.3333 NaN NaN NaN 0.1557 NaN 0.0000 NaN block of flats 0.1203 Panel No 2.0 0.0 2.0 0.0 0.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NaN NaN NaN NaN NaN NaN
355 100409 0 Cash loans M N Y 0 112500.000 454500.0 23337.0 454500.0 Unaccompanied Commercial associate Incomplete higher Single / not married House / apartment 0.026392 -10547 -1897 -4480.0 -3137 NaN 1 1 0 1 0 0 Drivers 1.0 2 2 FRIDAY 17 0 0 0 0 0 0 Industry: type 3 NaN 0.518013 0.340906 0.0866 NaN 0.9781 NaN NaN 0.0000 0.2069 0.1667 NaN NaN NaN 0.0763 NaN 0.0000 0.0882 NaN 0.9782 NaN NaN 0.0000 0.2069 0.1667 NaN NaN NaN 0.0795 NaN 0.0000 0.0874 NaN 0.9781 NaN NaN 0.00 0.2069 0.1667 NaN NaN NaN 0.0777 NaN 0.0000 NaN block of flats 0.0680 Block No 0.0 0.0 0.0 0.0 -1343.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 2.0
356 100410 0 Cash loans F Y N 0 180000.000 1258650.0 56277.0 1125000.0 Unaccompanied Working Secondary / secondary special Married House / apartment 0.032561 -14239 -4073 -8317.0 -5695 10.0 1 1 1 1 1 0 Managers 2.0 1 1 THURSDAY 17 0 0 0 0 1 1 Business Entity Type 3 NaN 0.776311 NaN 0.1335 0.1111 0.9776 0.6940 0.0247 0.1000 0.1552 0.2500 0.2917 0.0430 0.1088 0.1269 0.0000 0.0000 0.0840 0.1017 0.9777 0.7060 0.0116 0.0000 0.1379 0.1667 0.2083 0.0321 0.0735 0.0727 0.0000 0.0000 0.1348 0.1111 0.9776 0.6981 0.0248 0.10 0.1552 0.2500 0.2917 0.0438 0.1107 0.1291 0.0000 0.0000 reg oper spec account block of flats 0.1654 Panel No 5.0 0.0 5.0 0.0 -2461.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NaN NaN NaN NaN NaN NaN
357 100411 0 Cash loans M Y N 1 450000.000 1035832.5 33412.5 904500.0 Unaccompanied Working Higher education Married House / apartment 0.009334 -19410 -5545 -1006.0 -1822 9.0 1 1 1 1 0 0 Managers 3.0 2 2 WEDNESDAY 15 0 0 0 0 1 1 Self-employed NaN 0.609781 0.223831 0.0103 0.2148 0.9752 0.6600 0.0000 0.0000 0.1034 0.1250 0.0417 0.0468 0.0084 0.0236 0.0000 0.0844 0.0105 0.2229 0.9752 0.6733 0.0000 0.0000 0.1034 0.1250 0.0417 0.0479 0.0092 0.0246 0.0000 0.0893 0.0104 0.2148 0.9752 0.6645 0.0000 0.00 0.1034 0.1250 0.0417 0.0477 0.0086 0.0241 0.0000 0.0862 reg oper account block of flats 0.0369 Stone, brick No 0.0 0.0 0.0 0.0 -792.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 0.0
358 100412 0 Cash loans F Y Y 2 135000.000 318528.0 25294.5 252000.0 Unaccompanied Working Secondary / secondary special Married House / apartment 0.011703 -11039 -1865 -1379.0 -716 21.0 1 1 0 1 0 0 Medicine staff 4.0 2 2 THURSDAY 16 0 0 0 0 1 1 Medicine 0.411616 0.633435 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 0.0 0.0 0.0 0.0 -506.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NaN NaN NaN NaN NaN NaN
359 100413 0 Cash loans M N Y 0 90000.000 90000.0 9351.0 90000.0 Spouse, partner Pensioner Secondary / secondary special Married House / apartment 0.019101 -22768 365243 -4730.0 -5252 NaN 1 0 0 1 0 0 NaN 2.0 2 2 SATURDAY 12 0 0 0 0 0 0 XNA NaN 0.651336 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 5.0 0.0 5.0 0.0 -344.0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NaN NaN NaN NaN NaN NaN
360 100414 0 Cash loans F N Y 0 112500.000 533304.0 27360.0 405000.0 Unaccompanied Working Secondary / secondary special Single / not married House / apartment 0.018634 -12360 -4048 -5972.0 -4193 NaN 1 1 0 1 0 0 Sales staff 1.0 2 2 TUESDAY 10 0 0 0 1 1 0 Self-employed NaN 0.557008 0.651260 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 1.0 0.0 1.0 0.0 -2519.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 4.0
361 100415 0 Cash loans F N Y 2 112500.000 792162.0 38241.0 630000.0 Unaccompanied Working Secondary / secondary special Civil marriage House / apartment 0.014464 -13552 -2418 -755.0 -1252 NaN 1 1 0 1 0 0 Sales staff 4.0 2 2 MONDAY 5 0 0 0 0 0 0 Self-employed 0.779624 0.233826 0.832785 0.0825 0.0712 0.9757 0.6668 0.0302 0.0000 0.2069 0.1667 0.2083 0.0760 0.0672 0.0695 0.0000 0.0000 0.0840 0.0739 0.9757 0.6798 0.0305 0.0000 0.2069 0.1667 0.2083 0.0777 0.0735 0.0724 0.0000 0.0000 0.0833 0.0712 0.9757 0.6713 0.0304 0.00 0.2069 0.1667 0.2083 0.0773 0.0684 0.0708 0.0000 0.0000 reg oper spec account block of flats 0.0719 Panel No 0.0 0.0 0.0 0.0 -2275.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1.0 0.0 1.0 0.0 0.0 0.0
362 100417 0 Cash loans F N Y 0 180000.000 755190.0 36459.0 675000.0 Family Working Secondary / secondary special Widow House / apartment 0.035792 -16129 -879 -9171.0 -4716 NaN 1 1 0 1 0 0 Core staff 1.0 2 2 WEDNESDAY 11 0 0 0 0 1 1 Hotel NaN 0.355231 0.712155 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 0.0 0.0 0.0 0.0 -2779.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 2.0
363 100418 0 Cash loans F N N 0 135000.000 521280.0 31500.0 450000.0 Unaccompanied Working Secondary / secondary special Civil marriage House / apartment 0.009630 -13284 -1765 -562.0 -729 NaN 1 1 0 1 0 0 Sales staff 2.0 2 2 FRIDAY 17 0 0 0 0 0 0 Government 0.485289 0.737664 0.463275 0.0186 0.0000 0.9831 0.7688 0.0036 0.0000 0.1034 0.0417 0.0417 0.0000 0.0151 0.0195 0.0000 0.0000 0.0189 0.0000 0.9831 0.7779 0.0036 0.0000 0.1034 0.0417 0.0417 0.0000 0.0165 0.0203 0.0000 0.0000 0.0187 0.0000 0.9831 0.7719 0.0036 0.00 0.1034 0.0417 0.0417 0.0000 0.0154 0.0198 0.0000 0.0000 reg oper spec account block of flats 0.0173 Stone, brick No 3.0 1.0 3.0 0.0 -542.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 0.0
364 100419 0 Cash loans F N Y 0 112500.000 454500.0 13419.0 454500.0 Unaccompanied Pensioner Secondary / secondary special Separated House / apartment 0.031329 -20434 365243 -6328.0 -17 NaN 1 0 0 1 1 0 NaN 1.0 2 2 FRIDAY 9 0 0 0 0 0 0 XNA NaN 0.660829 0.384207 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 1.0 1.0 1.0 1.0 -1662.0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 0.0
365 100420 0 Cash loans F N Y 3 54000.000 91692.0 9198.0 81000.0 Unaccompanied Working Secondary / secondary special Married House / apartment 0.024610 -14283 -2191 -8330.0 -3851 NaN 1 1 0 1 0 0 Sales staff 5.0 2 2 FRIDAY 15 0 0 0 0 0 0 Business Entity Type 2 NaN 0.610426 0.355639 0.2227 0.0000 0.9856 0.8028 NaN 0.1200 0.1034 0.3333 0.0417 NaN 0.1807 0.2223 0.0039 0.0458 0.2269 0.0000 0.9856 0.8105 NaN 0.1208 0.1034 0.3333 0.0417 NaN 0.1974 0.2316 0.0039 0.0484 0.2248 0.0000 0.9856 0.8054 NaN 0.12 0.1034 0.3333 0.0417 NaN 0.1838 0.2263 0.0039 0.0467 reg oper account block of flats 0.2076 Stone, brick No 2.0 0.0 2.0 0.0 0.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 0.0
366 100421 0 Cash loans M Y N 0 450000.000 900000.0 32017.5 900000.0 Unaccompanied Commercial associate Higher education Single / not married Municipal apartment 0.072508 -19082 -2299 -4345.0 -2252 4.0 1 1 0 1 1 0 Managers 1.0 1 1 MONDAY 11 0 0 0 0 0 0 Business Entity Type 3 NaN 0.688239 0.389339 0.2160 0.0908 0.9841 0.7824 0.1120 0.2200 0.1034 0.5000 0.5417 0.0000 0.1698 0.2124 0.0290 0.0223 0.0756 0.0374 0.9747 0.6668 0.0340 0.0403 0.0345 0.3333 0.3750 0.0000 0.0661 0.0534 0.0000 0.0000 0.2181 0.0908 0.9841 0.7853 0.1127 0.22 0.1034 0.5000 0.5417 0.0000 0.1727 0.2162 0.0291 0.0227 reg oper account block of flats 0.4074 Block No 0.0 0.0 0.0 0.0 -502.0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 2.0
367 100423 0 Cash loans F Y Y 0 472500.000 1327648.5 42957.0 1039500.0 Unaccompanied Working Higher education Married House / apartment 0.046220 -16376 -1251 -673.0 -4310 6.0 1 1 0 1 0 1 High skill tech staff 2.0 1 1 WEDNESDAY 16 0 0 0 0 1 1 Self-employed 0.802867 0.050916 0.712155 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 2.0 0.0 2.0 0.0 -109.0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 1.0 1.0 3.0
368 100424 1 Cash loans M N Y 0 112500.000 117162.0 12433.5 103500.0 Unaccompanied Working Secondary / secondary special Separated House / apartment 0.018850 -18302 -136 -4005.0 -1281 NaN 1 1 0 1 0 0 Laborers 1.0 2 2 THURSDAY 11 0 0 0 0 1 1 Other NaN 0.500609 0.554947 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 0.0 0.0 0.0 0.0 -745.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 1.0
369 100425 0 Cash loans F Y Y 2 90000.000 688500.0 20259.0 688500.0 Family Working Higher education Married House / apartment 0.025164 -12982 -2850 -3476.0 -5139 64.0 1 1 0 1 0 0 Core staff 4.0 2 2 SATURDAY 8 0 0 0 0 0 0 Kindergarten 0.340206 0.335586 0.710674 0.0082 0.0000 0.9608 0.4628 0.0008 0.0000 0.0345 0.0833 0.1250 0.0086 0.0067 0.0060 0.0000 0.0000 0.0084 0.0000 0.9608 0.4838 0.0008 0.0000 0.0345 0.0833 0.1250 0.0088 0.0073 0.0063 0.0000 0.0000 0.0083 0.0000 0.9608 0.4700 0.0008 0.00 0.0345 0.0833 0.1250 0.0087 0.0068 0.0061 0.0000 0.0000 reg oper account block of flats 0.0082 Wooden No 3.0 0.0 3.0 0.0 0.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 0.0
370 100427 0 Cash loans F N N 1 67500.000 450000.0 30442.5 450000.0 Unaccompanied Working Higher education Married House / apartment 0.030755 -13455 -4971 -5851.0 -4246 NaN 1 1 0 1 0 0 Managers 3.0 2 2 THURSDAY 19 0 0 0 0 1 1 Postal NaN 0.396485 0.492060 0.0155 0.0382 0.9871 NaN NaN 0.0000 0.1034 0.0417 NaN 0.0074 NaN 0.0173 NaN 0.0000 0.0158 0.0397 0.9871 NaN NaN 0.0000 0.1034 0.0417 NaN 0.0076 NaN 0.0180 NaN 0.0000 0.0156 0.0382 0.9871 NaN NaN 0.00 0.1034 0.0417 NaN 0.0076 NaN 0.0176 NaN 0.0000 NaN block of flats 0.0150 Stone, brick No 0.0 0.0 0.0 0.0 -2458.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 0.0
371 100428 0 Cash loans F Y Y 0 90000.000 1224990.0 35946.0 877500.0 Unaccompanied Working Higher education Married House / apartment 0.007305 -19998 -2082 -393.0 -3499 18.0 1 1 0 1 0 0 Drivers 2.0 3 3 WEDNESDAY 6 0 0 0 0 0 0 Self-employed 0.851905 0.561288 0.537070 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 0.0 0.0 0.0 0.0 -2334.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 3.0 0.0 1.0
372 100429 0 Cash loans M N Y 0 180000.000 629325.0 26793.0 562500.0 Unaccompanied Working Secondary / secondary special Married House / apartment 0.035792 -15438 -936 -7215.0 -4030 NaN 1 1 0 1 0 0 Laborers 2.0 2 2 SUNDAY 12 0 0 0 0 0 0 Government 0.177098 0.262995 0.090221 0.1124 0.0575 0.9836 NaN NaN 0.1200 0.1034 0.3333 NaN 0.0919 NaN 0.1156 NaN 0.0010 0.1145 0.0597 0.9836 NaN NaN 0.1208 0.1034 0.3333 NaN 0.0940 NaN 0.1204 NaN 0.0011 0.1135 0.0575 0.9836 NaN NaN 0.12 0.1034 0.3333 NaN 0.0935 NaN 0.1177 NaN 0.0010 NaN block of flats 0.0911 Panel No 1.0 0.0 1.0 0.0 -543.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 0.0
373 100430 0 Cash loans F N Y 1 90000.000 157914.0 17136.0 139500.0 Unaccompanied Working Secondary / secondary special Married House / apartment 0.018850 -8650 -296 -832.0 -1314 NaN 1 1 0 1 0 0 Core staff 3.0 2 2 MONDAY 13 0 0 0 1 1 0 Other NaN 0.216048 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 0.0 0.0 0.0 0.0 -44.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 2.0
374 100431 0 Cash loans F N Y 1 157500.000 1724220.0 47542.5 1350000.0 Unaccompanied Working Secondary / secondary special Civil marriage House / apartment 0.022625 -19642 -6684 -2495.0 -3101 NaN 1 1 0 1 1 0 Laborers 3.0 2 2 FRIDAY 13 0 0 0 0 0 0 Business Entity Type 3 NaN 0.414193 0.000527 0.1031 0.1095 0.9831 0.7688 0.0116 0.0000 0.2069 0.1667 0.2083 0.0238 0.0832 0.0607 0.0039 0.0033 0.1050 0.1137 0.9831 0.7779 0.0117 0.0000 0.2069 0.1667 0.2083 0.0243 0.0909 0.0632 0.0039 0.0035 0.1041 0.1095 0.9831 0.7719 0.0116 0.00 0.2069 0.1667 0.2083 0.0242 0.0847 0.0618 0.0039 0.0033 reg oper account block of flats 0.0718 Panel No 0.0 0.0 0.0 0.0 -235.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 6.0
375 100432 0 Cash loans F N N 0 90000.000 495000.0 25272.0 495000.0 Unaccompanied Working Secondary / secondary special Married House / apartment 0.010556 -10347 -1276 -2127.0 -2163 NaN 1 1 1 1 1 0 Secretaries 2.0 3 3 WEDNESDAY 20 0 0 0 0 0 0 Government 0.243313 0.699217 0.733815 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 0.0 0.0 0.0 0.0 -506.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 1.0 2.0
376 100433 0 Cash loans F N Y 1 157500.000 472500.0 45454.5 454500.0 Unaccompanied Working Secondary / secondary special Civil marriage House / apartment 0.019689 -16048 -3879 -10146.0 -4217 NaN 1 1 0 1 0 0 Sales staff 3.0 2 2 MONDAY 11 0 0 0 0 0 0 Self-employed 0.732647 0.199543 0.785052 0.0412 0.0354 0.9742 NaN NaN 0.0000 0.0690 0.1667 NaN 0.0268 NaN 0.0312 NaN 0.0000 0.0420 0.0367 0.9742 NaN NaN 0.0000 0.0690 0.1667 NaN 0.0275 NaN 0.0325 NaN 0.0000 0.0416 0.0354 0.9742 NaN NaN 0.00 0.0690 0.1667 NaN 0.0273 NaN 0.0318 NaN 0.0000 NaN block of flats 0.0246 Stone, brick No 1.0 0.0 1.0 0.0 -1811.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 1.0
377 100434 0 Cash loans F N Y 1 202500.000 280170.0 30303.0 247500.0 Unaccompanied State servant Secondary / secondary special Separated Municipal apartment 0.072508 -17860 -1565 -11928.0 -1400 NaN 1 1 0 1 1 0 Medicine staff 2.0 1 1 WEDNESDAY 18 0 0 0 0 0 0 Other NaN 0.712768 0.387625 0.0784 0.0589 0.9737 NaN NaN 0.0200 0.0862 0.2500 NaN 0.0000 NaN 0.0606 NaN 0.0064 0.0756 0.0373 0.9737 NaN NaN 0.0000 0.0345 0.1667 NaN 0.0000 NaN 0.0536 NaN 0.0005 0.0791 0.0589 0.9737 NaN NaN 0.02 0.0862 0.2500 NaN 0.0000 NaN 0.0617 NaN 0.0065 NaN block of flats 0.0431 Panel No 1.0 0.0 1.0 0.0 -2652.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 1.0 3.0
378 100435 0 Cash loans F N Y 0 112500.000 450000.0 16294.5 450000.0 Unaccompanied Commercial associate Secondary / secondary special Separated House / apartment 0.035792 -16462 -4690 -3290.0 -15 NaN 1 1 0 1 0 0 NaN 1.0 2 2 SATURDAY 10 0 0 0 0 1 1 Industry: type 9 NaN 0.661887 0.680139 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 0.0 0.0 0.0 0.0 -2327.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 0.0
379 100436 0 Revolving loans F N Y 0 112500.000 405000.0 20250.0 405000.0 Unaccompanied Commercial associate Secondary / secondary special Married House / apartment 0.035792 -19345 -2418 -13244.0 -2891 NaN 1 1 0 1 0 0 Sales staff 2.0 2 2 FRIDAY 11 0 0 0 0 0 0 Business Entity Type 3 NaN 0.731873 0.657784 0.0619 0.0549 0.9737 0.6396 0.0064 0.0000 0.1034 0.1667 0.2083 0.0385 0.0504 0.0510 0.0000 0.0000 0.0630 0.0570 0.9737 0.6537 0.0065 0.0000 0.1034 0.1667 0.2083 0.0394 0.0551 0.0532 0.0000 0.0000 0.0625 0.0549 0.9737 0.6444 0.0065 0.00 0.1034 0.1667 0.2083 0.0392 0.0513 0.0520 0.0000 0.0000 reg oper account block of flats 0.0401 Panel No 0.0 0.0 0.0 0.0 -3258.0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 4.0
380 100437 0 Cash loans F N Y 1 67500.000 817560.0 30951.0 675000.0 Unaccompanied Pensioner Secondary / secondary special Married House / apartment 0.035792 -21401 365243 -3940.0 -4642 NaN 1 0 0 1 0 0 NaN 3.0 2 2 THURSDAY 13 0 0 0 0 0 0 XNA 0.741783 0.730751 0.636376 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 0.0 0.0 0.0 0.0 -407.0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 1.0 2.0
381 100439 1 Cash loans M N Y 0 81000.000 312840.0 22891.5 247500.0 Unaccompanied Working Secondary / secondary special Married House / apartment 0.018850 -8038 -609 -8038.0 -727 NaN 1 1 0 1 0 1 High skill tech staff 2.0 2 2 THURSDAY 17 0 0 0 1 1 0 Industry: type 9 NaN 0.482209 0.065110 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 6.0 0.0 6.0 0.0 -510.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 1.0 0.0 1.0
382 100440 0 Cash loans F N Y 0 40500.000 127350.0 7438.5 112500.0 Family Pensioner Secondary / secondary special Married House / apartment 0.007330 -23903 365243 -12295.0 -4332 NaN 1 0 0 1 0 0 NaN 2.0 2 2 FRIDAY 11 0 0 0 0 0 0 XNA NaN 0.096610 0.785052 0.0660 NaN 0.9836 NaN NaN 0.0000 0.1379 0.1250 0.0417 NaN 0.0538 0.0655 0.0000 NaN 0.0672 NaN 0.9836 NaN NaN 0.0000 0.1379 0.1250 0.0417 NaN 0.0588 0.0683 0.0000 NaN 0.0666 NaN 0.9836 NaN NaN 0.00 0.1379 0.1250 0.0417 NaN 0.0547 0.0667 0.0000 NaN reg oper account block of flats 0.0535 Stone, brick No 0.0 0.0 0.0 0.0 -861.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 2.0
383 100441 0 Cash loans F N Y 0 135000.000 269550.0 11416.5 225000.0 Unaccompanied Pensioner Secondary / secondary special Civil marriage House / apartment 0.009549 -20171 365243 -240.0 -3555 NaN 1 0 0 1 0 0 NaN 2.0 2 2 THURSDAY 16 0 0 0 0 0 0 XNA NaN 0.604382 0.669057 0.1237 0.1016 0.9776 0.6940 0.0424 0.0000 0.2069 0.1667 0.2083 0.1119 0.1009 0.1061 0.0000 0.0000 0.1261 0.1054 0.9777 0.7060 0.0428 0.0000 0.2069 0.1667 0.2083 0.1145 0.1102 0.1106 0.0000 0.0000 0.1249 0.1016 0.9776 0.6981 0.0427 0.00 0.2069 0.1667 0.2083 0.1139 0.1026 0.1080 0.0000 0.0000 reg oper account block of flats 0.0892 Stone, brick No 0.0 0.0 0.0 0.0 -1488.0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1.0 0.0 0.0 0.0 0.0 6.0
384 100442 0 Cash loans F N N 0 148500.000 805536.0 32076.0 720000.0 Children State servant Secondary / secondary special Married House / apartment 0.004960 -18715 -6991 -8455.0 -2255 NaN 1 1 0 1 1 0 Cooking staff 2.0 2 2 THURSDAY 16 0 0 0 1 1 0 Medicine 0.718754 0.772571 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 0.0 0.0 0.0 0.0 -1447.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NaN NaN NaN NaN NaN NaN
385 100443 0 Cash loans F N Y 0 144000.000 456273.0 23296.5 346500.0 Unaccompanied Pensioner Secondary / secondary special Widow House / apartment 0.010147 -23054 365243 -3975.0 -4748 NaN 1 0 0 1 0 0 NaN 1.0 2 2 WEDNESDAY 10 0 0 0 0 0 0 XNA NaN 0.427934 0.651260 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 1.0 1.0 1.0 1.0 0.0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 1.0
386 100448 0 Cash loans F N Y 2 202500.000 598486.5 25290.0 454500.0 Unaccompanied Working Secondary / secondary special Married House / apartment 0.008230 -11693 -3158 -2746.0 -3310 NaN 1 1 0 1 0 0 Sales staff 4.0 2 2 SUNDAY 12 0 0 0 0 0 0 Business Entity Type 3 NaN 0.597765 0.665855 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 0.0 0.0 0.0 0.0 -2700.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 1.0 1.0
387 100449 0 Cash loans F N Y 0 157500.000 942300.0 27679.5 675000.0 Unaccompanied Working Higher education Separated House / apartment 0.030755 -16832 -5544 -8718.0 -379 NaN 1 1 0 1 0 0 Laborers 1.0 2 2 FRIDAY 9 0 0 0 0 0 0 Business Entity Type 1 NaN 0.611881 0.486653 0.1639 0.0517 0.9791 0.7144 0.0140 0.0000 0.1034 0.1667 0.2083 0.0212 0.1328 0.0588 0.0039 0.0047 0.1670 0.0536 0.9791 0.7256 0.0141 0.0000 0.1034 0.1667 0.2083 0.0217 0.1451 0.0612 0.0039 0.0049 0.1655 0.0517 0.9791 0.7182 0.0141 0.00 0.1034 0.1667 0.2083 0.0216 0.1351 0.0598 0.0039 0.0048 reg oper account block of flats 0.0545 Stone, brick No 0.0 0.0 0.0 0.0 -849.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 0.0
388 100451 0 Cash loans F N Y 0 144000.000 446931.0 16978.5 369000.0 Unaccompanied Commercial associate Secondary / secondary special Married House / apartment 0.007020 -19501 -10988 -8368.0 -3013 NaN 1 1 0 1 0 0 Laborers 2.0 2 2 SATURDAY 13 0 0 0 0 1 1 Business Entity Type 2 NaN 0.652755 0.344155 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 1.0 0.0 1.0 0.0 -1649.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 5.0
389 100452 1 Cash loans M N Y 1 171000.000 1009566.0 36391.5 904500.0 Unaccompanied Working Secondary / secondary special Married House / apartment 0.025164 -18767 -2046 -6555.0 -2317 NaN 1 1 1 1 0 0 NaN 3.0 2 2 FRIDAY 8 0 0 0 0 0 0 Business Entity Type 3 NaN 0.266520 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 0.0 0.0 0.0 0.0 -1144.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NaN NaN NaN NaN NaN NaN
390 100453 0 Cash loans M Y N 2 247500.000 521280.0 27423.0 450000.0 Unaccompanied Working Higher education Married House / apartment 0.008866 -12191 -3298 -1371.0 -3948 6.0 1 1 0 1 0 0 NaN 4.0 2 2 TUESDAY 17 0 0 0 0 1 1 Business Entity Type 3 NaN 0.636496 0.681706 0.0021 NaN 0.9906 NaN NaN NaN 0.0690 0.0000 NaN NaN NaN 0.0042 NaN NaN 0.0021 NaN 0.9906 NaN NaN NaN 0.0690 0.0000 NaN NaN NaN 0.0044 NaN NaN 0.0021 NaN 0.9906 NaN NaN NaN 0.0690 0.0000 NaN NaN NaN 0.0043 NaN NaN NaN block of flats 0.0033 Wooden No 0.0 0.0 0.0 0.0 -1754.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 1.0
391 100454 0 Revolving loans M Y Y 3 607500.000 1350000.0 67500.0 1350000.0 Unaccompanied Commercial associate Higher education Married House / apartment 0.032561 -13530 -3269 -987.0 -4360 2.0 1 1 0 1 0 0 Managers 5.0 1 1 THURSDAY 10 0 0 0 0 0 0 Other NaN 0.623473 NaN 0.0608 0.0639 0.9503 0.3200 0.0168 0.1600 0.1379 0.2083 0.2500 0.0622 0.0488 0.0886 0.0077 0.0253 0.0609 0.0663 0.9503 0.3466 0.0169 0.1611 0.1379 0.2083 0.2500 0.0636 0.0533 0.0923 0.0078 0.0268 0.0614 0.0639 0.9503 0.3291 0.0169 0.16 0.1379 0.2083 0.2500 0.0633 0.0496 0.0902 0.0078 0.0258 reg oper account block of flats 0.0843 Stone, brick No 0.0 0.0 0.0 0.0 -2.0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 0.0
392 100455 0 Cash loans F Y Y 2 135000.000 830214.0 24273.0 693000.0 Unaccompanied Working Higher education Married House / apartment 0.030755 -11523 -3443 -37.0 -4059 8.0 1 1 0 1 0 0 Core staff 4.0 2 2 THURSDAY 12 0 0 0 0 0 0 Transport: type 2 0.183379 0.723860 0.703203 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 0.0 0.0 0.0 0.0 -464.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 0.0
393 100456 0 Cash loans M Y Y 1 211500.000 545040.0 26640.0 450000.0 Unaccompanied Working Secondary / secondary special Married House / apartment 0.019101 -12360 -914 -2092.0 -3968 14.0 1 1 0 1 0 0 Drivers 3.0 2 2 TUESDAY 12 0 0 0 0 1 1 Other 0.283292 0.548726 0.187389 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 0.0 0.0 0.0 0.0 -860.0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 1.0 0.0 2.0
394 100457 0 Revolving loans F Y Y 0 211500.000 270000.0 13500.0 270000.0 Unaccompanied Working Higher education Single / not married House / apartment 0.006008 -8922 -728 -8882.0 -66 7.0 1 1 0 1 1 0 Core staff 1.0 2 2 MONDAY 12 0 0 0 1 1 0 Trade: type 2 0.409581 0.656779 NaN 0.1113 0.0697 0.9881 0.8368 0.0712 0.1200 0.1034 0.3333 0.3750 0.0511 0.0908 0.1164 0.0000 0.0000 0.1134 0.0724 0.9881 0.8432 0.0718 0.1208 0.1034 0.3333 0.3750 0.0522 0.0992 0.1213 0.0000 0.0000 0.1124 0.0697 0.9881 0.8390 0.0717 0.12 0.1034 0.3333 0.3750 0.0520 0.0923 0.1185 0.0000 0.0000 org spec account block of flats 0.1063 Stone, brick No NaN NaN NaN NaN -638.0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NaN NaN NaN NaN NaN NaN
395 100458 0 Cash loans F N N 0 90000.000 808650.0 26217.0 675000.0 Family Pensioner Secondary / secondary special Married House / apartment 0.020713 -20646 365243 -12781.0 -3849 NaN 1 0 0 1 0 0 NaN 2.0 3 3 THURSDAY 8 0 0 0 0 0 0 XNA 0.575941 0.552811 0.662638 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 0.0 0.0 0.0 0.0 -238.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 0.0
396 100459 0 Revolving loans M Y Y 0 270000.000 225000.0 11250.0 225000.0 Unaccompanied Commercial associate Secondary / secondary special Single / not married House / apartment 0.046220 -15832 -459 -8346.0 -4726 6.0 1 1 0 1 1 0 Drivers 1.0 1 1 THURSDAY 10 1 1 0 0 0 0 Business Entity Type 3 NaN 0.734636 0.256706 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 0.0 0.0 0.0 0.0 -1031.0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 2.0 0.0 0.0
397 100460 0 Revolving loans F N N 0 315000.000 540000.0 27000.0 540000.0 Unaccompanied Commercial associate Higher education Single / not married Rented apartment 0.014464 -9842 -157 -2291.0 -2291 NaN 1 1 0 1 0 0 NaN 1.0 2 2 FRIDAY 13 0 1 1 0 0 0 Business Entity Type 3 0.439873 0.349457 0.633032 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN -404.0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 0.0
398 100461 0 Cash loans F Y Y 0 252000.000 288873.0 16713.0 238500.0 Unaccompanied Working Secondary / secondary special Married House / apartment 0.007114 -15868 -2134 -7595.0 -4536 15.0 1 1 0 1 0 0 Core staff 2.0 2 2 TUESDAY 10 0 0 0 0 0 0 School 0.678348 0.262055 0.511892 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 0.0 0.0 0.0 0.0 -1603.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 3.0
399 100462 0 Cash loans M N N 0 111919.500 152820.0 7911.0 135000.0 Unaccompanied Commercial associate Secondary / secondary special Civil marriage House / apartment 0.018209 -16395 -8001 -457.0 -4574 NaN 1 1 1 1 1 0 Laborers 2.0 3 3 WEDNESDAY 11 0 0 0 0 0 0 Business Entity Type 2 NaN 0.490758 0.465069 NaN 0.4013 0.9786 NaN NaN NaN 0.2069 0.1667 NaN NaN NaN 0.0976 NaN 0.0707 NaN 0.4165 0.9786 NaN NaN NaN 0.2069 0.1667 NaN NaN NaN 0.1017 NaN 0.0748 NaN 0.4013 0.9786 NaN NaN NaN 0.2069 0.1667 NaN NaN NaN 0.0994 NaN 0.0721 NaN NaN 0.0921 Panel No 1.0 0.0 1.0 0.0 -2463.0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 0.0
400 100463 0 Cash loans F N N 0 112500.000 263686.5 15268.5 238500.0 Unaccompanied Pensioner Secondary / secondary special Married House / apartment 0.004960 -14402 365243 -4217.0 -2403 NaN 1 0 0 1 1 0 NaN 2.0 2 2 FRIDAY 17 0 0 0 0 0 0 XNA NaN 0.649103 0.700184 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 2.0 0.0 2.0 0.0 -1347.0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 5.0
401 100464 0 Cash loans M N N 0 112500.000 345510.0 17640.0 247500.0 Unaccompanied Working Secondary / secondary special Single / not married House / apartment 0.008625 -9825 -819 -3990.0 -2491 NaN 1 1 0 1 0 1 Drivers 1.0 2 2 TUESDAY 15 0 0 0 0 0 0 Self-employed NaN 0.361107 0.767523 0.0392 0.0198 0.9791 0.7144 0.0218 0.0400 0.0345 0.3333 0.3750 0.0171 0.0303 0.0371 0.0077 0.0082 0.0399 0.0205 0.9791 0.7256 0.0220 0.0403 0.0345 0.3333 0.3750 0.0175 0.0331 0.0386 0.0078 0.0086 0.0396 0.0198 0.9791 0.7182 0.0219 0.04 0.0345 0.3333 0.3750 0.0174 0.0308 0.0377 0.0078 0.0083 reg oper account block of flats 0.0428 Stone, brick No 0.0 0.0 0.0 0.0 -1542.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 1.0
402 100465 0 Cash loans M N N 0 135000.000 545040.0 20677.5 450000.0 Unaccompanied Working Secondary / secondary special Married House / apartment 0.016612 -15783 -5452 -8176.0 -3819 NaN 1 1 0 1 1 0 Laborers 2.0 2 2 TUESDAY 10 0 0 0 0 0 0 Business Entity Type 2 0.626080 0.603388 NaN 0.0320 0.0000 0.9598 0.4492 0.0279 0.0000 0.1379 0.1250 0.1667 0.0233 0.0261 0.0380 0.0000 0.0000 0.0326 0.0000 0.9598 0.4708 0.0282 0.0000 0.1379 0.1250 0.1667 0.0238 0.0285 0.0396 0.0000 0.0000 0.0323 0.0000 0.9598 0.4566 0.0281 0.00 0.1379 0.1250 0.1667 0.0237 0.0265 0.0387 0.0000 0.0000 reg oper account block of flats 0.0452 Stone, brick No 0.0 0.0 0.0 0.0 -2207.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 2.0
403 100467 0 Cash loans F N N 0 81000.000 225000.0 13284.0 225000.0 Unaccompanied Working Secondary / secondary special Single / not married House / apartment 0.028663 -17935 -474 -2053.0 -1453 NaN 1 1 0 1 0 0 Laborers 1.0 2 2 FRIDAY 13 0 0 0 1 1 0 Trade: type 3 NaN 0.425965 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 0.0 0.0 0.0 0.0 0.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 NaN NaN NaN NaN NaN NaN
404 100468 0 Cash loans F Y N 0 112500.000 900000.0 23872.5 900000.0 Unaccompanied Pensioner Secondary / secondary special Married House / apartment 0.010006 -18287 365243 -229.0 -1454 2.0 1 0 0 1 0 0 NaN 2.0 2 2 WEDNESDAY 8 0 0 0 0 0 0 XNA NaN 0.243041 NaN 0.0021 NaN 0.9990 0.9864 NaN 0.0000 0.0000 0.0000 0.0417 NaN 0.0017 0.0023 0.0000 NaN 0.0021 NaN 0.9990 0.9869 NaN 0.0000 0.0000 0.0000 0.0417 NaN 0.0018 0.0024 0.0000 NaN 0.0021 NaN 0.9990 0.9866 NaN 0.00 0.0000 0.0000 0.0417 NaN 0.0017 0.0023 0.0000 NaN NaN block of flats 0.0018 Block No 3.0 2.0 3.0 0.0 -6.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NaN NaN NaN NaN NaN NaN
405 100469 0 Revolving loans M Y Y 0 450000.000 1350000.0 67500.0 1350000.0 Unaccompanied Commercial associate Higher education Married House / apartment 0.014464 -10485 -663 -4.0 -2007 4.0 1 1 0 1 0 0 Managers 2.0 2 2 WEDNESDAY 4 0 0 0 0 0 0 Transport: type 4 0.521039 0.491983 NaN 0.0866 0.0000 0.9608 0.4628 0.0041 0.0000 0.1379 0.0833 0.1250 0.0614 0.0706 0.0385 0.0000 0.0000 0.0882 0.0000 0.9608 0.4838 0.0042 0.0000 0.1379 0.0833 0.1250 0.0628 0.0771 0.0401 0.0000 0.0000 0.0874 0.0000 0.9608 0.4700 0.0042 0.00 0.1379 0.0833 0.1250 0.0625 0.0718 0.0392 0.0000 0.0000 reg oper account block of flats 0.0320 Block No 3.0 0.0 3.0 0.0 -64.0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NaN NaN NaN NaN NaN NaN
406 100470 0 Cash loans F Y Y 0 225000.000 1345500.0 39469.5 1345500.0 Unaccompanied Commercial associate Higher education Married House / apartment 0.026392 -15601 -3880 -9642.0 -2242 0.0 1 1 0 1 0 0 NaN 2.0 2 2 SATURDAY 14 0 0 0 0 0 0 Business Entity Type 2 NaN 0.619853 0.657784 0.1412 NaN 0.9841 NaN NaN 0.1600 0.1379 0.3750 NaN 0.1125 NaN 0.1449 NaN 0.0651 0.1439 NaN 0.9841 NaN NaN 0.1611 0.1379 0.3750 NaN 0.1150 NaN 0.1509 NaN 0.0690 0.1426 NaN 0.9841 NaN NaN 0.16 0.1379 0.3750 NaN 0.1144 NaN 0.1475 NaN 0.0665 NaN block of flats 0.1281 Stone, brick No 3.0 0.0 2.0 0.0 0.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 1.0 1.0 0.0
407 100471 0 Cash loans F Y Y 1 112500.000 697500.0 35743.5 697500.0 Unaccompanied Working Secondary / secondary special Married House / apartment 0.030755 -15150 -392 -70.0 -6223 1.0 1 1 0 1 0 0 Laborers 3.0 2 2 SATURDAY 16 0 0 0 0 0 0 Services NaN 0.627433 NaN 0.0722 0.0782 0.9816 0.7484 0.0082 0.0000 0.1379 0.1667 0.2083 0.0525 0.0588 0.0642 0.0000 0.0000 0.0735 0.0812 0.9816 0.7583 0.0083 0.0000 0.1379 0.1667 0.2083 0.0537 0.0643 0.0669 0.0000 0.0000 0.0729 0.0782 0.9816 0.7518 0.0083 0.00 0.1379 0.1667 0.2083 0.0534 0.0599 0.0653 0.0000 0.0000 reg oper account block of flats 0.0550 Stone, brick No 3.0 0.0 3.0 0.0 0.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NaN NaN NaN NaN NaN NaN
408 100472 1 Cash loans M Y Y 1 135000.000 545040.0 26509.5 450000.0 Unaccompanied Working Secondary / secondary special Married House / apartment 0.008474 -13923 -519 -10704.0 -4739 11.0 1 1 1 1 1 0 Laborers 3.0 2 2 THURSDAY 15 0 0 0 0 0 0 Self-employed NaN 0.365807 0.243186 0.0619 0.0626 0.9791 0.7144 0.0243 0.0000 0.1379 0.1667 0.2083 0.0000 0.0504 0.0537 0.0000 0.0000 0.0630 0.0649 0.9791 0.7256 0.0246 0.0000 0.1379 0.1667 0.2083 0.0000 0.0551 0.0560 0.0000 0.0000 0.0625 0.0626 0.9791 0.7182 0.0245 0.00 0.1379 0.1667 0.2083 0.0000 0.0513 0.0547 0.0000 0.0000 reg oper account block of flats 0.0577 Panel No 1.0 1.0 1.0 1.0 -2527.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 0.0
409 100473 0 Cash loans M Y Y 0 405000.000 1483231.5 51687.0 1354500.0 Unaccompanied Commercial associate Higher education Married House / apartment 0.010500 -16245 -2015 -3319.0 -4513 7.0 1 1 0 1 0 0 Managers 2.0 3 3 WEDNESDAY 12 0 0 0 0 0 0 Business Entity Type 3 0.517231 0.599814 0.374021 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 0.2386 NaN No 1.0 0.0 1.0 0.0 -1139.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 1.0 1.0 3.0
410 100474 0 Revolving loans M Y N 0 450000.000 900000.0 45000.0 900000.0 Unaccompanied Commercial associate Higher education Married House / apartment 0.072508 -23045 -2652 -1813.0 -4227 15.0 1 1 0 1 0 0 Managers 2.0 1 1 TUESDAY 14 0 0 0 0 0 0 Electricity 0.844487 0.718292 NaN 0.1515 0.0560 0.9955 NaN NaN 0.0800 0.0345 1.0000 NaN 0.0000 NaN 0.3393 NaN 0.0000 0.1544 0.0581 0.9955 NaN NaN 0.0806 0.0345 1.0000 NaN 0.0000 NaN 0.3535 NaN 0.0000 0.1530 0.0560 0.9955 NaN NaN 0.08 0.0345 1.0000 NaN 0.0000 NaN 0.3454 NaN 0.0000 NaN block of flats 0.2669 Monolithic No 1.0 0.0 1.0 0.0 -207.0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NaN NaN NaN NaN NaN NaN
411 100475 0 Revolving loans F Y Y 0 405000.000 495000.0 24750.0 495000.0 Unaccompanied Commercial associate Higher education Separated House / apartment 0.032561 -20165 -362 -91.0 -3577 5.0 1 1 0 1 0 0 Accountants 1.0 1 1 THURSDAY 17 0 0 0 0 0 0 Self-employed NaN 0.779766 0.176653 0.2216 0.1495 0.9771 NaN NaN 0.2400 0.2069 0.3333 NaN NaN NaN 0.2225 NaN 0.0032 0.2258 0.1552 0.9772 NaN NaN 0.2417 0.2069 0.3333 NaN NaN NaN 0.2318 NaN 0.0033 0.2238 0.1495 0.9771 NaN NaN 0.24 0.2069 0.3333 NaN NaN NaN 0.2265 NaN 0.0032 NaN block of flats 0.1757 NaN No 1.0 0.0 1.0 0.0 -565.0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 1.0
412 100476 0 Cash loans M Y Y 1 225000.000 509922.0 31324.5 472500.0 Unaccompanied Working Secondary / secondary special Married House / apartment 0.008625 -18469 -304 -9289.0 -1992 3.0 1 1 0 1 1 0 Laborers 3.0 2 2 TUESDAY 10 0 1 1 0 1 1 Other NaN 0.366020 0.528093 0.1113 0.0744 0.9846 NaN NaN 0.1200 0.1034 0.3333 NaN 0.0141 NaN 0.1142 NaN 0.0003 0.1134 0.0772 0.9846 NaN NaN 0.1208 0.1034 0.3333 NaN 0.0144 NaN 0.1190 NaN 0.0003 0.1124 0.0744 0.9846 NaN NaN 0.12 0.1034 0.3333 NaN 0.0143 NaN 0.1162 NaN 0.0003 NaN block of flats 0.1175 Panel No 1.0 0.0 1.0 0.0 -271.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 1.0 0.0
413 100477 1 Cash loans M Y Y 1 112500.000 284400.0 22599.0 225000.0 Spouse, partner Working Secondary / secondary special Married House / apartment 0.015221 -13715 -151 -6193.0 -3754 12.0 1 1 1 1 0 0 Drivers 3.0 2 2 SATURDAY 8 0 0 0 0 1 1 Business Entity Type 3 0.303693 0.616027 0.079060 0.0175 0.0000 0.9786 0.7076 0.0017 0.0000 0.0690 0.0417 0.0833 NaN 0.0134 0.0136 0.0039 0.0023 0.0179 0.0000 0.9786 0.7190 0.0017 0.0000 0.0690 0.0417 0.0833 NaN 0.0147 0.0142 0.0039 0.0024 0.0177 0.0000 0.9786 0.7115 0.0017 0.00 0.0690 0.0417 0.0833 NaN 0.0137 0.0138 0.0039 0.0023 reg oper account block of flats 0.0121 Stone, brick No 0.0 0.0 0.0 0.0 -1364.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 3.0
414 100478 0 Cash loans F N Y 1 90000.000 393543.0 20223.0 328500.0 Unaccompanied Working Secondary / secondary special Married With parents 0.031329 -11935 -5348 -973.0 -2814 NaN 1 1 0 1 0 0 Laborers 3.0 2 2 FRIDAY 14 0 0 0 0 1 1 Business Entity Type 3 0.541069 0.523880 0.644679 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 2.0 1.0 2.0 1.0 0.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 2.0 3.0
415 100479 0 Revolving loans M Y Y 0 90000.000 180000.0 9000.0 180000.0 Unaccompanied Working Secondary / secondary special Single / not married House / apartment 0.018801 -9496 -189 -4182.0 -2153 64.0 1 1 0 1 0 0 Laborers 1.0 2 2 SUNDAY 7 0 0 0 0 1 1 Business Entity Type 3 0.084902 0.573584 0.452534 0.0495 0.0525 0.9742 0.6464 0.0072 0.0000 0.1034 0.1250 0.0417 0.0130 0.0403 0.0397 0.0000 0.0000 0.0504 0.0545 0.9742 0.6602 0.0072 0.0000 0.1034 0.1250 0.0417 0.0133 0.0441 0.0414 0.0000 0.0000 0.0500 0.0525 0.9742 0.6511 0.0072 0.00 0.1034 0.1250 0.0417 0.0132 0.0410 0.0404 0.0000 0.0000 reg oper account block of flats 0.0352 Stone, brick No 0.0 0.0 0.0 0.0 0.0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 1.0
416 100480 0 Cash loans M N N 0 450000.000 473760.0 50269.5 450000.0 Group of people Commercial associate Secondary / secondary special Single / not married House / apartment 0.046220 -10046 -1444 -4798.0 -1533 NaN 1 1 1 1 0 0 IT staff 1.0 1 1 THURSDAY 16 0 1 1 0 1 1 Business Entity Type 3 NaN 0.664242 NaN 0.1146 NaN 0.9776 NaN NaN 0.0000 0.2483 0.1667 NaN NaN NaN 0.0672 NaN 0.1040 0.1250 NaN 0.9757 NaN NaN 0.0000 0.2759 0.1667 NaN NaN NaN 0.0595 NaN 0.0038 0.1239 NaN 0.9757 NaN NaN 0.00 0.2759 0.1667 NaN NaN NaN 0.0710 NaN 0.1311 NaN block of flats 0.0457 Panel No 0.0 0.0 0.0 0.0 -1350.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NaN NaN NaN NaN NaN NaN
417 100481 0 Revolving loans M Y Y 0 180000.000 540000.0 27000.0 540000.0 Family Working Secondary / secondary special Married House / apartment 0.011657 -17599 -9542 -426.0 -1148 3.0 1 1 0 1 1 0 Laborers 2.0 1 1 WEDNESDAY 12 0 0 0 1 1 0 Business Entity Type 3 NaN 0.655076 0.775155 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 2.0 0.0 2.0 0.0 -1713.0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 0.0
418 100482 0 Cash loans F Y Y 0 112500.000 445500.0 16924.5 445500.0 Unaccompanied Commercial associate Secondary / secondary special Civil marriage Office apartment 0.022800 -18579 -890 -12020.0 -2125 21.0 1 1 0 1 0 0 Medicine staff 2.0 2 2 WEDNESDAY 8 0 0 0 0 1 1 Medicine NaN 0.306789 0.698668 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 0.0 0.0 0.0 0.0 -526.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 3.0 3.0 0.0 0.0 6.0
419 100485 1 Revolving loans M Y N 1 270000.000 810000.0 40500.0 810000.0 Unaccompanied Working Secondary / secondary special Married House / apartment 0.026392 -11353 -651 -2513.0 -3962 8.0 1 1 1 1 1 0 Security staff 3.0 2 2 TUESDAY 9 0 0 0 0 0 0 Security NaN 0.335981 0.577969 0.1918 0.1417 0.9950 NaN NaN 0.0800 0.0690 0.3750 NaN NaN NaN 0.2408 NaN 0.0000 0.1954 0.1471 0.9950 NaN NaN 0.0806 0.0690 0.3750 NaN NaN NaN 0.2509 NaN 0.0000 0.1936 0.1417 0.9950 NaN NaN 0.08 0.0690 0.3750 NaN NaN NaN 0.2452 NaN 0.0000 NaN block of flats 0.1894 Stone, brick No 0.0 0.0 0.0 0.0 -443.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 0.0
420 100486 0 Cash loans F N Y 0 58500.000 172021.5 12991.5 148500.0 Unaccompanied Pensioner Secondary / secondary special Widow House / apartment 0.015221 -23794 365243 -9471.0 -4250 NaN 1 0 0 1 1 0 NaN 1.0 2 2 TUESDAY 9 0 0 0 0 0 0 XNA NaN 0.448734 0.651260 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 5.0 1.0 5.0 0.0 -1419.0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 3.0
421 100487 0 Cash loans M Y N 0 121500.000 630747.0 22783.5 544500.0 Family Pensioner Secondary / secondary special Married House / apartment 0.016612 -16476 365243 -4646.0 -32 1.0 1 0 0 1 0 0 NaN 2.0 2 2 MONDAY 13 0 0 0 0 0 0 XNA NaN 0.343412 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 0.0 0.0 0.0 0.0 -572.0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 NaN NaN NaN NaN NaN NaN
422 100488 0 Cash loans F Y N 1 270000.000 364896.0 24075.0 315000.0 Unaccompanied Commercial associate Higher education Single / not married House / apartment 0.046220 -15300 -435 -586.0 -4024 4.0 1 1 0 1 0 0 Core staff 2.0 1 1 SATURDAY 10 0 0 0 0 0 0 Other 0.582155 0.645088 0.676993 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 2.0 0.0 2.0 0.0 -4.0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 1.0 0.0 0.0
423 100489 0 Cash loans M N Y 0 90000.000 238500.0 25816.5 238500.0 Unaccompanied Working Secondary / secondary special Married House / apartment 0.010556 -19833 -3953 -159.0 -3383 NaN 1 1 1 1 1 0 Laborers 2.0 3 3 SUNDAY 14 0 0 0 0 0 0 Business Entity Type 2 0.710824 0.408432 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 0.0 0.0 0.0 0.0 -3177.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 1.0 0.0 1.0
424 100490 1 Cash loans M N N 0 135000.000 755190.0 38686.5 675000.0 Family Working Secondary / secondary special Separated House / apartment 0.009630 -9684 -2354 -3801.0 -2344 NaN 1 1 1 1 0 0 Security staff 1.0 2 2 WEDNESDAY 11 0 1 1 0 1 1 Security 0.203371 0.281799 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 0.0 0.0 0.0 0.0 -730.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NaN NaN NaN NaN NaN NaN
425 100491 0 Cash loans M Y N 0 193500.000 314100.0 17167.5 225000.0 Unaccompanied Working Secondary / secondary special Married With parents 0.003818 -12018 -644 -1199.0 -560 64.0 1 1 0 1 0 0 Drivers 2.0 2 2 FRIDAY 8 0 0 0 0 1 1 Self-employed 0.150956 0.082627 NaN 0.0247 0.0362 0.9970 NaN NaN 0.0000 0.0345 0.0833 NaN NaN NaN 0.0213 NaN 0.0727 0.0252 0.0375 0.9970 NaN NaN 0.0000 0.0345 0.0833 NaN NaN NaN 0.0222 NaN 0.0769 0.0250 0.0362 0.9970 NaN NaN 0.00 0.0345 0.0833 NaN NaN NaN 0.0217 NaN 0.0742 NaN block of flats 0.0326 Monolithic No 1.0 0.0 1.0 0.0 -809.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NaN NaN NaN NaN NaN NaN
426 100492 0 Cash loans F N Y 3 450000.000 716161.5 57550.5 634500.0 Unaccompanied State servant Higher education Separated House / apartment 0.006629 -12662 -892 -6586.0 -1849 NaN 1 1 0 1 0 0 Core staff 4.0 2 2 THURSDAY 5 0 0 0 0 0 0 University 0.647561 0.625817 0.307737 0.0818 0.0000 0.9901 0.8368 NaN 0.0800 0.0917 0.3054 NaN 0.0146 NaN 0.1000 NaN 0.0123 0.0315 0.0000 0.9881 0.8432 NaN 0.0403 0.1034 0.3333 NaN 0.0127 NaN 0.0726 NaN 0.0000 0.1062 0.0000 0.9886 0.8390 NaN 0.08 0.1034 0.3333 NaN 0.0149 NaN 0.0998 NaN 0.0000 reg oper account block of flats 0.0548 Panel No 4.0 0.0 4.0 0.0 -554.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 5.0
427 100493 0 Cash loans F Y Y 0 270000.000 1288350.0 41692.5 1125000.0 Unaccompanied Working Higher education Married House / apartment 0.035792 -18651 -2593 -5769.0 -2207 16.0 1 1 0 1 1 0 NaN 2.0 2 2 THURSDAY 11 0 0 0 0 0 0 Self-employed NaN 0.692166 0.349055 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 4.0 0.0 4.0 0.0 -2814.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 2.0 0.0 2.0
428 100495 0 Cash loans F Y N 1 112500.000 832500.0 29623.5 832500.0 Children Working Secondary / secondary special Married House / apartment 0.006305 -13494 -2298 -1138.0 -577 13.0 1 1 0 1 0 0 NaN 3.0 3 3 TUESDAY 10 0 0 0 0 0 0 Medicine NaN 0.536266 0.782608 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 4.0 2.0 4.0 2.0 -395.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 0.0
429 100496 0 Cash loans F N Y 0 450000.000 1125000.0 33025.5 1125000.0 Unaccompanied Working Secondary / secondary special Married House / apartment 0.026392 -18627 -2190 -1340.0 -2114 NaN 1 1 0 1 1 0 Sales staff 2.0 2 2 FRIDAY 14 0 0 0 0 0 0 Business Entity Type 3 NaN 0.567395 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 0.0 0.0 0.0 0.0 -1802.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 1.0
430 100497 0 Revolving loans M N Y 0 135000.000 180000.0 9000.0 180000.0 Other_B Commercial associate Secondary / secondary special Single / not married House / apartment 0.009175 -8956 -401 -3606.0 -1629 NaN 1 1 1 1 1 0 Low-skill Laborers 1.0 2 2 SUNDAY 17 0 0 0 0 0 0 Business Entity Type 2 0.301636 0.719295 0.729567 0.0333 0.0460 0.9821 0.7552 0.0051 0.0000 0.1262 0.0692 0.1108 0.0756 0.0272 0.0396 0.0000 0.0000 0.0042 0.0000 0.9786 0.7190 0.0000 0.0000 0.0345 0.0000 0.0417 0.0498 0.0037 0.0036 0.0000 0.0000 0.0187 0.0000 0.9816 0.7518 0.0024 0.00 0.1034 0.0417 0.0833 0.0612 0.0154 0.0175 0.0000 0.0000 reg oper account block of flats 0.0027 Stone, brick No 0.0 0.0 0.0 0.0 -823.0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 0.0
431 100498 0 Cash loans M N Y 0 112500.000 485190.0 24903.0 405000.0 Unaccompanied Working Secondary / secondary special Married House / apartment 0.020713 -16468 -1544 -5023.0 -21 NaN 1 1 0 1 0 0 NaN 2.0 3 3 WEDNESDAY 6 0 0 0 0 0 0 Business Entity Type 3 NaN 0.361334 0.275000 0.0928 0.0973 0.9886 0.8436 0.0149 0.0000 0.2069 0.1667 0.2083 0.0899 0.0748 0.0990 0.0039 0.0047 0.0945 0.1009 0.9886 0.8497 0.0150 0.0000 0.2069 0.1667 0.2083 0.0919 0.0817 0.1031 0.0039 0.0050 0.0937 0.0973 0.9886 0.8457 0.0150 0.00 0.2069 0.1667 0.2083 0.0914 0.0761 0.1007 0.0039 0.0048 reg oper spec account block of flats 0.0778 Panel No 0.0 0.0 0.0 0.0 -1388.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 4.0
432 100499 0 Cash loans F Y Y 0 225000.000 678996.0 36963.0 540000.0 Unaccompanied Working Secondary / secondary special Civil marriage House / apartment 0.010032 -13229 -1210 -7237.0 -4662 17.0 1 1 0 1 0 0 NaN 2.0 2 2 WEDNESDAY 10 0 0 0 0 0 0 Business Entity Type 3 NaN 0.589716 0.786267 0.1856 0.1403 0.9791 NaN NaN 0.2000 0.1724 0.3333 NaN 0.0800 NaN 0.1273 NaN NaN 0.1513 0.1157 0.9791 NaN NaN 0.1611 0.1379 0.3333 NaN 0.0679 NaN 0.1065 NaN NaN 0.1874 0.1403 0.9791 NaN NaN 0.20 0.1724 0.3333 NaN 0.0814 NaN 0.1296 NaN NaN NaN block of flats 0.2076 Panel No 4.0 0.0 4.0 0.0 -840.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 4.0
433 100500 0 Cash loans F N N 0 202500.000 840951.0 35631.0 679500.0 Unaccompanied Commercial associate Secondary / secondary special Single / not married Municipal apartment 0.032561 -18522 -4322 -1778.0 -1940 NaN 1 1 0 1 0 0 Laborers 1.0 1 1 TUESDAY 9 0 0 0 0 0 0 Business Entity Type 3 0.725739 0.668059 0.634706 0.2247 0.2167 0.9836 NaN NaN 0.3600 0.1724 0.3333 NaN 0.0298 NaN 0.1971 NaN 0.0064 0.1197 0.2249 0.9806 NaN NaN 0.3625 0.0345 0.3333 NaN 0.0305 NaN 0.0696 NaN 0.0000 0.2269 0.2167 0.9836 NaN NaN 0.36 0.1724 0.3333 NaN 0.0303 NaN 0.2006 NaN 0.0065 NaN block of flats 0.2661 Stone, brick No 6.0 0.0 6.0 0.0 -1815.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 4.0
434 100501 0 Cash loans M N N 1 247500.000 1125000.0 29808.0 1125000.0 Unaccompanied Working Higher education Single / not married House / apartment 0.007274 -13816 -1870 -29.0 -3537 NaN 1 1 0 1 0 0 Laborers 2.0 2 2 WEDNESDAY 10 0 0 0 0 0 0 Electricity NaN 0.529778 0.634706 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 2.0 1.0 2.0 0.0 -2.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 1.0 0.0 0.0
435 100502 0 Cash loans F Y Y 0 202500.000 485640.0 41674.5 450000.0 Unaccompanied Commercial associate Higher education Married Municipal apartment 0.022625 -16121 -3159 -3020.0 -997 1.0 1 1 0 1 0 0 Core staff 2.0 2 2 SATURDAY 12 0 0 0 0 0 0 Kindergarten NaN 0.750615 0.597192 0.1485 NaN 0.9776 NaN NaN 0.0000 0.0690 0.1667 NaN 0.0594 NaN 0.0463 NaN 0.2808 0.1513 NaN 0.9777 NaN NaN 0.0000 0.0690 0.1667 NaN 0.0608 NaN 0.0482 NaN 0.2973 0.1499 NaN 0.9776 NaN NaN 0.00 0.0690 0.1667 NaN 0.0605 NaN 0.0471 NaN 0.2867 NaN specific housing 0.0645 Stone, brick No 0.0 0.0 0.0 0.0 -1039.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 1.0
436 100503 0 Cash loans F N Y 0 189000.000 876019.5 34870.5 783000.0 Unaccompanied Pensioner Secondary / secondary special Married House / apartment 0.009175 -22524 365243 -8604.0 -5021 NaN 1 0 0 1 1 0 NaN 2.0 2 2 THURSDAY 13 0 0 0 0 0 0 XNA NaN 0.618440 0.329655 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 0.0 0.0 0.0 0.0 -287.0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 1.0 0.0 6.0
437 100504 0 Cash loans F N Y 0 157500.000 187704.0 12672.0 148500.0 Unaccompanied Commercial associate Secondary / secondary special Single / not married House / apartment 0.019689 -18271 -206 -7168.0 -1823 NaN 1 1 1 1 1 0 Core staff 1.0 2 2 FRIDAY 8 0 0 0 0 0 0 Business Entity Type 3 NaN 0.542177 0.266457 0.0082 NaN 0.9583 0.4288 NaN 0.0000 0.0690 0.0417 NaN NaN NaN 0.0063 NaN NaN 0.0084 NaN 0.9583 0.4512 NaN 0.0000 0.0690 0.0417 NaN NaN NaN 0.0066 NaN NaN 0.0083 NaN 0.9583 0.4364 NaN 0.00 0.0690 0.0417 NaN NaN NaN 0.0064 NaN NaN not specified block of flats 0.0071 Wooden No 0.0 0.0 0.0 0.0 -2878.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 5.0 0.0 1.0
438 100505 0 Cash loans M Y Y 3 225000.000 675000.0 24799.5 675000.0 Unaccompanied Working Secondary / secondary special Married House / apartment 0.022625 -17222 -3723 -5550.0 -760 7.0 1 1 1 1 0 0 Security staff 5.0 2 2 MONDAY 13 0 0 0 0 1 1 Transport: type 1 NaN 0.643479 0.656158 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 1.0 1.0 1.0 1.0 -2998.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 4.0
439 100506 0 Cash loans M Y Y 0 112500.000 345645.0 16240.5 243000.0 Unaccompanied Working Secondary / secondary special Married House / apartment 0.006305 -14671 -1977 -7984.0 -4376 20.0 1 1 0 1 0 0 NaN 2.0 3 3 SATURDAY 4 0 0 0 0 1 1 Business Entity Type 3 NaN 0.545150 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 0.0 0.0 0.0 0.0 -613.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NaN NaN NaN NaN NaN NaN
440 100507 0 Cash loans M N N 1 202500.000 450000.0 42075.0 450000.0 Unaccompanied Commercial associate Secondary / secondary special Civil marriage With parents 0.010032 -7747 -139 -2577.0 -380 NaN 1 1 0 1 0 0 Sales staff 3.0 2 2 THURSDAY 15 0 0 0 1 0 1 Trade: type 2 NaN 0.266327 0.586740 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 0.0 0.0 0.0 0.0 -263.0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 1.0
441 100508 0 Cash loans F Y Y 0 450000.000 1971072.0 62019.0 1800000.0 Spouse, partner Commercial associate Higher education Married House / apartment 0.032561 -18268 -2569 -4292.0 -1699 1.0 1 1 0 1 1 1 Managers 2.0 1 1 WEDNESDAY 13 0 0 0 0 0 0 Trade: type 3 0.746108 0.774368 0.344155 0.1814 0.1071 0.9901 0.8708 0.0000 0.2200 0.1207 0.4167 0.4167 0.0265 0.0975 0.1525 0.0000 0.0023 0.1218 0.0574 0.9896 0.8759 0.0000 0.1208 0.1034 0.3750 0.4167 0.0271 0.1065 0.1409 0.0000 0.0000 0.1832 0.1071 0.9901 0.8725 0.0000 0.22 0.1207 0.4167 0.4167 0.0269 0.0992 0.1552 0.0000 0.0023 org spec account block of flats 0.2298 Panel No 0.0 0.0 0.0 0.0 -523.0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 4.0 1.0
442 100509 0 Cash loans F N Y 0 90000.000 339241.5 12312.0 238500.0 Unaccompanied Pensioner Secondary / secondary special Separated House / apartment 0.035792 -21987 365243 -8482.0 -4704 NaN 1 0 0 1 0 0 NaN 1.0 2 2 WEDNESDAY 9 0 0 0 0 0 0 XNA NaN 0.694500 0.652897 0.0608 0.0578 0.9762 0.6736 0.0052 0.0000 0.1034 0.1667 0.2083 0.0354 0.0496 0.0509 0.0000 0.0000 0.0620 0.0600 0.9762 0.6864 0.0052 0.0000 0.1034 0.1667 0.2083 0.0363 0.0542 0.0530 0.0000 0.0000 0.0614 0.0578 0.9762 0.6780 0.0052 0.00 0.1034 0.1667 0.2083 0.0361 0.0504 0.0518 0.0000 0.0000 reg oper account block of flats 0.0400 Stone, brick No 0.0 0.0 0.0 0.0 -1723.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 2.0 2.0
443 100511 0 Cash loans F N N 0 85500.000 454500.0 19255.5 454500.0 Unaccompanied Working Secondary / secondary special Married House / apartment 0.018634 -11519 -1563 -4980.0 -4134 NaN 1 1 1 1 1 0 Sales staff 2.0 2 2 THURSDAY 18 0 0 0 0 0 0 Self-employed 0.412063 0.391806 0.475850 0.2320 NaN 0.9876 NaN NaN 0.0800 0.0690 0.3333 NaN NaN NaN 0.1525 NaN 0.0299 0.2363 NaN 0.9876 NaN NaN 0.0806 0.0690 0.3333 NaN NaN NaN 0.1589 NaN 0.0317 0.2342 NaN 0.9876 NaN NaN 0.08 0.0690 0.3333 NaN NaN NaN 0.1553 NaN 0.0305 NaN block of flats 0.1970 Stone, brick No 5.0 1.0 5.0 0.0 -787.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 0.0
444 100513 0 Cash loans F N Y 0 180000.000 848745.0 36090.0 675000.0 Unaccompanied Commercial associate Secondary / secondary special Married House / apartment 0.035792 -18344 -7364 -3255.0 -1891 NaN 1 1 0 1 0 0 NaN 2.0 2 2 WEDNESDAY 17 0 0 0 1 1 0 Business Entity Type 3 0.750608 0.625079 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 6.0 0.0 6.0 0.0 -586.0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 NaN NaN NaN NaN NaN NaN
445 100514 0 Cash loans M N Y 0 270000.000 265306.5 28701.0 252000.0 Unaccompanied Pensioner Higher education Married House / apartment 0.018029 -15187 365243 -1856.0 -4116 NaN 1 0 0 1 0 0 NaN 2.0 3 3 FRIDAY 10 0 0 0 0 0 0 XNA 0.296512 0.597712 0.461482 0.0402 0.0270 0.9970 NaN 0.0128 0.0400 0.0345 0.3333 NaN 0.4228 0.0303 0.0425 0.0116 0.0119 0.0410 0.0280 0.9970 NaN 0.0129 0.0403 0.0345 0.3333 NaN 0.4324 0.0331 0.0443 0.0117 0.0126 0.0406 0.0270 0.9970 NaN 0.0128 0.04 0.0345 0.3333 NaN 0.4301 0.0308 0.0433 0.0116 0.0122 NaN block of flats 0.0430 NaN No 1.0 0.0 1.0 0.0 -1534.0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 4.0
446 100515 0 Cash loans F N Y 0 157500.000 1032093.0 43857.0 922500.0 Unaccompanied Commercial associate Secondary / secondary special Married House / apartment 0.031329 -17668 -900 -1890.0 -1200 NaN 1 1 0 1 1 0 Laborers 2.0 2 2 FRIDAY 17 0 0 0 0 0 0 Business Entity Type 3 NaN 0.587492 0.639708 0.0722 0.0623 0.9906 0.8708 0.0156 0.0800 0.0690 0.3333 0.3750 0.0966 0.0588 0.0767 0.0000 0.0000 0.0735 0.0646 0.9906 0.8759 0.0157 0.0806 0.0690 0.3333 0.3750 0.0988 0.0643 0.0799 0.0000 0.0000 0.0729 0.0623 0.9906 0.8725 0.0157 0.08 0.0690 0.3333 0.3750 0.0983 0.0599 0.0781 0.0000 0.0000 reg oper spec account block of flats 0.0688 Stone, brick No 0.0 0.0 0.0 0.0 -1059.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 3.0
447 100516 0 Cash loans F N N 0 157500.000 117000.0 11700.0 117000.0 Unaccompanied Pensioner Secondary / secondary special Single / not married House / apartment 0.028663 -20792 365243 -4205.0 -4203 NaN 1 0 0 1 1 0 NaN 1.0 2 2 THURSDAY 9 0 0 0 0 0 0 XNA NaN 0.674395 0.798137 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 10.0 0.0 10.0 0.0 -1533.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 3.0 2.0
448 100518 0 Revolving loans F N Y 0 117000.000 202500.0 10125.0 202500.0 Family Pensioner Secondary / secondary special Widow House / apartment 0.007330 -22681 365243 -13564.0 -4046 NaN 1 0 0 1 0 0 NaN 1.0 2 2 TUESDAY 11 0 0 0 0 0 0 XNA NaN 0.580362 NaN 0.0825 0.0777 0.9752 NaN NaN 0.0000 0.1379 0.1667 NaN 0.1242 NaN 0.0471 NaN 0.0000 0.0840 0.0806 0.9752 NaN NaN 0.0000 0.1379 0.1667 NaN 0.1271 NaN 0.0490 NaN 0.0000 0.0833 0.0777 0.9752 NaN NaN 0.00 0.1379 0.1667 NaN 0.1264 NaN 0.0479 NaN 0.0000 NaN block of flats 0.0553 Panel No 1.0 0.0 0.0 0.0 -1339.0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NaN NaN NaN NaN NaN NaN
449 100519 0 Cash loans F N N 0 112500.000 625536.0 27684.0 540000.0 Unaccompanied Pensioner Secondary / secondary special Widow Municipal apartment 0.007114 -19031 365243 -7369.0 -2569 NaN 1 0 0 1 1 0 NaN 1.0 2 2 TUESDAY 15 0 0 0 0 0 0 XNA NaN 0.273554 0.533482 0.0649 0.0005 0.9747 0.6532 0.0065 0.0000 0.1379 0.1250 0.1667 0.0555 0.0521 0.0511 0.0039 0.0054 0.0662 0.0005 0.9747 0.6668 0.0066 0.0000 0.1379 0.1250 0.1667 0.0567 0.0569 0.0533 0.0039 0.0057 0.0656 0.0005 0.9747 0.6578 0.0065 0.00 0.1379 0.1250 0.1667 0.0564 0.0530 0.0520 0.0039 0.0055 reg oper account block of flats 0.0402 Stone, brick No 0.0 0.0 0.0 0.0 -1581.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 4.0
450 100520 0 Revolving loans F N Y 0 135000.000 270000.0 13500.0 270000.0 Unaccompanied Working Secondary / secondary special Single / not married House / apartment 0.007305 -20143 -2572 -614.0 -2091 NaN 1 1 0 1 0 0 Laborers 1.0 3 3 TUESDAY 11 0 0 0 0 0 0 Business Entity Type 3 0.504049 0.326079 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 7.0 0.0 7.0 0.0 -588.0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NaN NaN NaN NaN NaN NaN
451 100521 0 Revolving loans F N N 0 157500.000 225000.0 11250.0 225000.0 Unaccompanied Pensioner Secondary / secondary special Married House / apartment 0.008474 -20523 365243 -8472.0 -4043 NaN 1 0 0 1 0 0 NaN 2.0 2 2 FRIDAY 13 0 0 0 0 0 0 XNA 0.713867 0.543957 0.126101 0.0278 0.0570 0.9757 NaN NaN 0.0000 0.1034 0.1667 NaN 0.0256 NaN 0.0591 NaN 0.0231 0.0284 0.0592 0.9757 NaN NaN 0.0000 0.1034 0.1667 NaN 0.0262 NaN 0.0615 NaN 0.0244 0.0281 0.0570 0.9757 NaN NaN 0.00 0.1034 0.1667 NaN 0.0261 NaN 0.0601 NaN 0.0236 NaN block of flats 0.0465 Stone, brick No 0.0 0.0 0.0 0.0 -207.0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 3.0 0.0 2.0
452 100522 0 Cash loans F Y Y 1 180000.000 343683.0 20898.0 261000.0 Children Working Secondary / secondary special Married House / apartment 0.018801 -10933 -2309 -4757.0 -1777 1.0 1 1 0 1 0 0 NaN 3.0 2 2 TUESDAY 12 0 0 0 0 0 0 Business Entity Type 3 NaN 0.533186 0.385915 0.0948 0.2162 0.9891 0.8504 0.0794 0.0000 0.2414 0.1667 0.2083 0.0394 0.0773 0.0552 0.0000 0.0000 0.0966 0.2244 0.9891 0.8563 0.0801 0.0000 0.2414 0.1667 0.2083 0.0403 0.0845 0.0575 0.0000 0.0000 0.0958 0.2162 0.9891 0.8524 0.0799 0.00 0.2414 0.1667 0.2083 0.0401 0.0787 0.0562 0.0000 0.0000 reg oper account block of flats 0.0825 Stone, brick No 2.0 0.0 2.0 0.0 -150.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 0.0
453 100523 0 Cash loans M Y Y 0 225000.000 1096020.0 52857.0 900000.0 Unaccompanied Commercial associate Secondary / secondary special Married House / apartment 0.046220 -18278 -1808 -3518.0 -1830 27.0 1 1 0 1 0 0 Low-skill Laborers 2.0 1 1 SATURDAY 10 0 0 0 0 1 1 Business Entity Type 3 NaN 0.496651 0.627991 0.1052 0.0584 0.9831 0.7688 0.0363 0.0800 0.0345 0.3333 0.3750 0.0524 0.0832 0.0724 0.0116 0.0655 0.1071 0.0606 0.9831 0.7779 0.0366 0.0806 0.0345 0.3333 0.3750 0.0536 0.0909 0.0755 0.0117 0.0693 0.1062 0.0584 0.9831 0.7719 0.0365 0.08 0.0345 0.3333 0.3750 0.0533 0.0847 0.0737 0.0116 0.0668 reg oper account specific housing 0.0768 Stone, brick No 0.0 0.0 0.0 0.0 -2166.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 2.0 2.0
454 100524 0 Cash loans M N Y 0 112500.000 284400.0 20740.5 225000.0 Unaccompanied Working Secondary / secondary special Married House / apartment 0.006207 -10936 -3186 -173.0 -3568 NaN 1 1 0 1 0 0 NaN 2.0 2 2 TUESDAY 9 0 0 0 0 0 0 Business Entity Type 3 NaN 0.682145 NaN 0.0996 0.0180 0.9886 0.8436 0.0069 0.0360 0.0407 0.3029 0.3142 0.0522 0.0808 0.0715 0.0021 0.0068 0.1124 0.0000 0.9861 0.8171 0.0051 0.0403 0.0345 0.3333 0.3750 0.0262 0.0983 0.0464 0.0000 0.0000 0.1114 0.0000 0.9876 0.8323 0.0070 0.04 0.0345 0.3333 0.3750 0.0414 0.0915 0.0770 0.0000 0.0000 reg oper account block of flats 0.0364 Stone, brick No 1.0 1.0 1.0 1.0 -516.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NaN NaN NaN NaN NaN NaN
455 100525 0 Cash loans M N Y 0 157500.000 215640.0 11421.0 180000.0 Unaccompanied Working Secondary / secondary special Civil marriage House / apartment 0.005144 -15191 -116 -1666.0 -4947 NaN 1 1 0 1 0 0 Laborers 2.0 2 2 MONDAY 12 0 0 0 0 0 0 Self-employed NaN 0.499275 NaN 0.1490 0.0416 0.9806 0.7348 0.0156 0.0000 0.0690 0.1667 0.1250 0.1750 0.1202 0.0551 0.0058 0.0042 0.1502 0.0000 0.9806 0.7452 0.0100 0.0000 0.0345 0.1667 0.0417 0.0000 0.1313 0.0574 0.0000 0.0000 0.1504 0.0416 0.9806 0.7383 0.0157 0.00 0.0690 0.1667 0.1250 0.1781 0.1223 0.0561 0.0058 0.0043 reg oper account block of flats 0.0555 Stone, brick No 2.0 0.0 2.0 0.0 0.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 0.0
456 100526 0 Cash loans M Y N 1 144000.000 90000.0 8253.0 90000.0 Unaccompanied Commercial associate Secondary / secondary special Married House / apartment 0.003122 -13227 -668 -7304.0 -4830 27.0 1 1 0 1 1 0 Drivers 3.0 3 3 FRIDAY 19 0 0 0 0 0 0 Self-employed NaN 0.460102 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 1.0 1.0 1.0 1.0 -278.0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 NaN NaN NaN NaN NaN NaN
457 100527 0 Revolving loans F N N 0 112500.000 270000.0 13500.0 270000.0 Unaccompanied Commercial associate Higher education Civil marriage House / apartment 0.026392 -9393 -992 -4145.0 -2081 NaN 1 1 0 1 1 0 Accountants 2.0 2 2 WEDNESDAY 15 0 0 0 1 1 0 Bank 0.239312 0.696210 0.669057 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN -187.0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 1.0
458 100528 0 Cash loans F N Y 1 90000.000 675000.0 19867.5 675000.0 Unaccompanied Working Secondary / secondary special Married House / apartment 0.020246 -14608 -4624 -514.0 -6085 NaN 1 1 0 1 0 0 NaN 3.0 3 3 FRIDAY 6 0 0 0 0 1 1 Medicine NaN 0.527756 0.572683 0.0082 NaN NaN 0.7008 0.0014 NaN NaN 0.0417 NaN NaN NaN 0.0063 NaN NaN 0.0084 NaN NaN 0.7125 0.0014 NaN NaN 0.0417 NaN NaN NaN 0.0066 NaN NaN 0.0083 NaN NaN 0.7048 0.0014 NaN NaN 0.0417 NaN NaN NaN 0.0064 NaN NaN NaN NaN 0.0057 NaN No 1.0 0.0 1.0 0.0 -1218.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 1.0 4.0
459 100529 0 Cash loans F N Y 0 135000.000 269550.0 17820.0 225000.0 Family Working Higher education Single / not married With parents 0.028663 -8857 -348 -3456.0 -1507 NaN 1 1 0 1 0 0 Core staff 1.0 2 2 SUNDAY 8 0 0 0 0 0 0 Self-employed NaN 0.229966 NaN 0.0711 0.0000 0.9737 0.6396 0.0102 0.0000 0.1724 0.1667 0.2083 0.0633 0.0572 0.0575 0.0039 0.0367 0.0725 0.0000 0.9737 0.6537 0.0103 0.0000 0.1724 0.1667 0.2083 0.0648 0.0624 0.0599 0.0039 0.0389 0.0718 0.0000 0.9737 0.6444 0.0103 0.00 0.1724 0.1667 0.2083 0.0644 0.0581 0.0586 0.0039 0.0375 reg oper account block of flats 0.0532 Stone, brick No 0.0 0.0 0.0 0.0 0.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 NaN NaN NaN NaN NaN NaN
460 100530 0 Cash loans F Y Y 0 135000.000 770292.0 34807.5 688500.0 Unaccompanied Commercial associate Secondary / secondary special Married House / apartment 0.010556 -18035 -3802 -3669.0 -1594 14.0 1 1 0 1 0 0 Sales staff 2.0 3 3 WEDNESDAY 9 0 0 0 0 1 1 Other NaN 0.445199 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 0.0 0.0 0.0 0.0 -268.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 1.0 0.0 1.0
461 100531 0 Cash loans F Y Y 2 112500.000 904500.0 25002.0 904500.0 Family Working Higher education Married Rented apartment 0.030755 -12349 -589 -4182.0 -4270 10.0 1 1 0 1 0 0 Core staff 4.0 2 2 SATURDAY 14 0 0 0 1 1 0 Trade: type 3 0.360726 0.657473 0.417100 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 0.0 0.0 0.0 0.0 -218.0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 1.0 0.0 2.0
462 100532 0 Cash loans M Y Y 0 99000.000 277969.5 13086.0 229500.0 Family Working Secondary / secondary special Civil marriage House / apartment 0.008625 -18437 -117 -6339.0 -1979 17.0 1 1 0 1 0 0 Laborers 2.0 2 2 TUESDAY 14 0 0 0 0 1 1 Business Entity Type 3 NaN 0.290609 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 3.0 0.0 3.0 0.0 -226.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NaN NaN NaN NaN NaN NaN
463 100533 0 Revolving loans M N Y 0 121500.000 382500.0 19125.0 382500.0 Unaccompanied Working Secondary / secondary special Married House / apartment 0.028663 -15363 -5359 -2917.0 -4121 NaN 1 1 1 1 0 0 Laborers 2.0 2 2 SUNDAY 12 0 0 0 0 0 0 Construction 0.625823 0.795727 NaN 0.0361 0.1030 0.9697 NaN NaN 0.0000 0.1379 0.1250 NaN 0.0000 NaN 0.0504 NaN 0.0692 0.0368 0.1069 0.9697 NaN NaN 0.0000 0.1379 0.1250 NaN 0.0000 NaN 0.0525 NaN 0.0733 0.0364 0.1030 0.9697 NaN NaN 0.00 0.1379 0.1250 NaN 0.0000 NaN 0.0513 NaN 0.0707 NaN block of flats 0.0948 Stone, brick No 0.0 0.0 0.0 0.0 -2787.0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 1.0 1.0
464 100534 0 Cash loans F N N 0 157500.000 256500.0 14449.5 256500.0 Unaccompanied Commercial associate Secondary / secondary special Married House / apartment 0.028663 -13986 -4363 -425.0 -3512 NaN 1 1 0 1 0 0 Laborers 2.0 2 2 TUESDAY 13 0 0 0 0 0 0 Business Entity Type 2 0.498334 0.543452 0.372334 0.0619 0.0585 0.9757 NaN NaN 0.0000 0.1034 0.1667 NaN NaN NaN 0.0514 NaN 0.0000 0.0630 0.0607 0.9757 NaN NaN 0.0000 0.1034 0.1667 NaN NaN NaN 0.0536 NaN 0.0000 0.0625 0.0585 0.9757 NaN NaN 0.00 0.1034 0.1667 NaN NaN NaN 0.0523 NaN 0.0000 NaN NaN 0.0522 Mixed No 0.0 0.0 0.0 0.0 -377.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 1.0 2.0 1.0 6.0
465 100535 0 Cash loans M Y N 0 90000.000 225000.0 11619.0 225000.0 Unaccompanied Working Secondary / secondary special Civil marriage House / apartment 0.006671 -20488 -1352 -10561.0 -3885 16.0 1 1 0 1 0 0 Laborers 2.0 2 2 THURSDAY 15 0 0 0 0 1 1 Industry: type 9 0.618375 0.541368 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 0.0 0.0 0.0 0.0 -2246.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 1.0 0.0 3.0
466 100536 0 Cash loans F Y Y 2 99000.000 787131.0 42066.0 679500.0 Unaccompanied Commercial associate Secondary / secondary special Married House / apartment 0.019101 -14357 -1683 -7323.0 -4375 64.0 1 1 0 1 0 0 Sales staff 4.0 2 2 TUESDAY 10 0 0 0 0 0 0 Agriculture 0.405907 0.723493 0.681706 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 0.0 0.0 0.0 0.0 -1964.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 2.0 0.0 4.0
467 100537 0 Cash loans F N Y 1 180000.000 1044724.5 37656.0 936000.0 Family Working Higher education Married House / apartment 0.020713 -11418 -3351 -5597.0 -2717 NaN 1 1 0 1 0 0 Accountants 3.0 3 2 SATURDAY 11 0 0 0 0 0 0 Medicine 0.175839 0.457398 0.396220 0.1753 0.1396 0.9896 NaN NaN 0.2000 0.1724 0.3750 NaN 0.1454 NaN 0.2276 NaN 0.0000 0.1786 0.1449 0.9896 NaN NaN 0.2014 0.1724 0.3750 NaN 0.1487 NaN 0.2371 NaN 0.0000 0.1770 0.1396 0.9896 NaN NaN 0.20 0.1724 0.3750 NaN 0.1479 NaN 0.2317 NaN 0.0000 NaN block of flats 0.2001 Panel No 6.0 0.0 6.0 0.0 -2295.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 2.0
468 100538 0 Cash loans M Y N 1 157500.000 450000.0 24412.5 450000.0 Unaccompanied Working Secondary / secondary special Married House / apartment 0.030755 -16060 -3031 -3116.0 -3076 15.0 1 1 0 1 0 0 Security staff 3.0 2 2 WEDNESDAY 10 0 0 0 0 1 1 Industry: type 3 0.456317 0.413111 0.093225 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 1.0 0.0 1.0 0.0 -2254.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 1.0 0.0 0.0 2.0
469 100539 0 Cash loans M N Y 0 184500.000 225000.0 22252.5 225000.0 Unaccompanied State servant Secondary / secondary special Single / not married House / apartment 0.024610 -14678 -3353 -2155.0 -5501 NaN 1 1 0 1 1 0 Core staff 1.0 2 2 MONDAY 20 0 0 0 0 0 0 Military 0.650523 0.729299 0.773896 0.0330 0.0316 0.9727 0.6260 NaN 0.0000 0.0690 0.1250 0.1667 0.0119 0.0269 0.0256 0.0000 0.0000 0.0336 0.0328 0.9727 0.6406 NaN 0.0000 0.0690 0.1250 0.1667 0.0122 0.0294 0.0267 0.0000 0.0000 0.0333 0.0316 0.9727 0.6310 NaN 0.00 0.0690 0.1250 0.1667 0.0121 0.0274 0.0261 0.0000 0.0000 NaN block of flats 0.0217 Stone, brick No 0.0 0.0 0.0 0.0 -1831.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 4.0
470 100540 1 Cash loans F N Y 1 157500.000 338832.0 26901.0 292500.0 Unaccompanied State servant Secondary / secondary special Single / not married House / apartment 0.018029 -9289 -1003 -823.0 -1952 NaN 1 1 0 1 0 0 Medicine staff 2.0 3 3 TUESDAY 11 0 0 0 0 0 0 Medicine 0.040684 0.306506 0.396220 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 0.0 0.0 0.0 0.0 -683.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 1.0 0.0 4.0
471 100541 1 Cash loans F Y N 2 180000.000 900000.0 43299.0 900000.0 Unaccompanied Working Secondary / secondary special Single / not married House / apartment 0.018209 -10616 -1065 -3964.0 -2700 17.0 1 1 1 1 0 0 Sales staff 3.0 3 3 MONDAY 14 0 0 0 0 1 1 Self-employed NaN 0.084550 0.144648 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 1.0 0.0 1.0 0.0 -697.0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 1.0 0.0 1.0
472 100542 0 Cash loans F N Y 0 112500.000 156339.0 11245.5 130500.0 Unaccompanied Working Secondary / secondary special Married House / apartment 0.011703 -16388 -8923 -7741.0 -4192 NaN 1 1 0 1 0 0 Medicine staff 2.0 2 2 TUESDAY 11 0 0 0 1 0 1 Medicine 0.526415 0.728171 0.391055 0.0928 0.0813 0.9891 0.8504 0.0107 0.0000 0.2069 0.1667 0.0417 0.0936 0.0756 0.0846 0.0000 0.0000 0.0945 0.0843 0.9891 0.8563 0.0108 0.0000 0.2069 0.1667 0.0417 0.0958 0.0826 0.0882 0.0000 0.0000 0.0937 0.0813 0.9891 0.8524 0.0108 0.00 0.2069 0.1667 0.0417 0.0953 0.0770 0.0861 0.0000 0.0000 reg oper spec account block of flats 0.0724 Panel No 0.0 0.0 0.0 0.0 0.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 4.0 2.0
473 100543 0 Cash loans F N Y 0 157500.000 225000.0 26703.0 225000.0 Family Pensioner Secondary / secondary special Separated House / apartment 0.016612 -20173 365243 -9054.0 -3717 NaN 1 0 0 1 0 1 NaN 1.0 2 2 MONDAY 15 0 0 0 0 0 0 XNA NaN 0.588043 0.283712 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 0.0 0.0 0.0 0.0 -1227.0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 1.0 1.0
474 100544 0 Cash loans F N Y 0 96750.000 252000.0 13176.0 252000.0 Unaccompanied Commercial associate Secondary / secondary special Single / not married House / apartment 0.018209 -13583 -117 -2717.0 -2727 NaN 1 1 0 1 0 0 Sales staff 1.0 3 3 TUESDAY 10 0 0 0 1 1 0 Trade: type 3 0.389731 0.034576 0.215182 0.1113 0.1000 0.9871 0.8232 0.1627 0.0000 0.2069 0.1667 0.0417 0.0192 0.0899 0.0876 0.0039 0.0850 0.1134 0.1038 0.9871 0.8301 0.1642 0.0000 0.2069 0.1667 0.0417 0.0196 0.0983 0.0913 0.0039 0.0900 0.1124 0.1000 0.9871 0.8256 0.1637 0.00 0.2069 0.1667 0.0417 0.0195 0.0915 0.0892 0.0039 0.0868 reg oper account block of flats 0.0889 Panel No 0.0 0.0 0.0 0.0 -82.0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 1.0 0.0
475 100545 0 Cash loans F N Y 0 144000.000 1012500.0 29736.0 1012500.0 Unaccompanied Working Secondary / secondary special Married House / apartment 0.035792 -19058 -10089 -1320.0 -1153 NaN 1 1 0 1 0 0 NaN 2.0 2 2 TUESDAY 9 0 0 0 0 1 1 Emergency NaN 0.682751 0.556727 0.0639 NaN 0.9980 NaN NaN 0.0000 0.1379 0.1667 NaN NaN NaN 0.0784 NaN 0.0753 0.0651 NaN 0.9980 NaN NaN 0.0000 0.1379 0.1667 NaN NaN NaN 0.0817 NaN 0.0797 0.0645 NaN 0.9980 NaN NaN 0.00 0.1379 0.1667 NaN NaN NaN 0.0799 NaN 0.0769 NaN block of flats 0.0958 Block No 0.0 0.0 0.0 0.0 -1516.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 3.0
476 100546 0 Cash loans M N Y 0 67500.000 508495.5 21672.0 454500.0 Unaccompanied Pensioner Secondary / secondary special Married House / apartment 0.031329 -23539 365243 -6397.0 -4677 NaN 1 0 0 1 0 0 NaN 2.0 2 2 THURSDAY 8 0 0 0 0 0 0 XNA NaN 0.162192 0.551381 0.0021 NaN 0.9841 NaN NaN NaN 0.0345 0.0000 NaN NaN NaN 0.0018 NaN 0.0000 0.0021 NaN 0.9841 NaN NaN NaN 0.0345 0.0000 NaN NaN NaN 0.0019 NaN 0.0000 0.0021 NaN 0.9841 NaN NaN NaN 0.0345 0.0000 NaN NaN NaN 0.0018 NaN 0.0000 NaN block of flats 0.0014 Stone, brick No 0.0 0.0 0.0 0.0 -2368.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 0.0
477 100547 1 Cash loans M Y N 0 211500.000 450000.0 21888.0 450000.0 Unaccompanied Working Secondary / secondary special Married House / apartment 0.026392 -10037 -148 -781.0 -2714 1.0 1 1 1 1 1 1 High skill tech staff 2.0 2 2 SATURDAY 11 0 1 1 0 1 1 Other NaN 0.220605 0.273565 0.0247 NaN 0.9767 NaN NaN 0.0000 0.1034 0.0417 NaN NaN NaN 0.0199 NaN 0.0000 0.0252 NaN 0.9767 NaN NaN 0.0000 0.1034 0.0417 NaN NaN NaN 0.0207 NaN 0.0000 0.0250 NaN 0.9767 NaN NaN 0.00 0.1034 0.0417 NaN NaN NaN 0.0202 NaN 0.0000 NaN block of flats 0.0156 Stone, brick No 0.0 0.0 0.0 0.0 -1401.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 3.0
478 100549 0 Cash loans F N Y 0 225000.000 545040.0 28350.0 450000.0 Other_A Commercial associate Secondary / secondary special Single / not married House / apartment 0.032561 -16540 -3845 -1484.0 -55 NaN 1 1 0 1 0 0 Private service staff 1.0 1 1 TUESDAY 16 0 0 0 0 0 0 Trade: type 3 NaN 0.193733 NaN 0.0289 NaN 0.9290 0.0276 NaN NaN 0.1034 0.1667 NaN 0.0176 0.0151 0.0487 0.0386 0.0581 0.0294 NaN 0.9290 0.0657 NaN NaN 0.1034 0.1667 NaN 0.0180 0.0165 0.0507 0.0389 0.0615 0.0291 NaN 0.9290 0.0406 NaN NaN 0.1034 0.1667 NaN 0.0179 0.0154 0.0496 0.0388 0.0593 reg oper account block of flats 0.0509 Stone, brick No 0.0 0.0 0.0 0.0 0.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 2.0
479 100550 0 Cash loans F Y Y 0 247500.000 669591.0 24178.5 508500.0 Family Commercial associate Higher education Married House / apartment 0.035792 -13768 -2722 -6902.0 -4257 21.0 1 1 0 1 0 0 Laborers 2.0 2 2 WEDNESDAY 10 0 0 0 0 0 0 Business Entity Type 3 0.507368 0.639940 0.513694 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 0.0 0.0 0.0 0.0 -1791.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 2.0
480 100554 0 Cash loans F Y Y 0 225000.000 1125000.0 31068.0 1125000.0 Unaccompanied Commercial associate Higher education Married Office apartment 0.028663 -11980 -486 -2360.0 -4334 7.0 1 1 0 1 0 0 Managers 2.0 2 2 THURSDAY 9 0 0 0 0 0 0 Business Entity Type 3 NaN 0.623869 0.775155 0.0753 NaN 0.9935 0.9116 NaN NaN 0.0345 0.5833 0.6250 NaN NaN 0.1216 NaN 0.1230 0.0767 NaN 0.9935 0.9151 NaN NaN 0.0345 0.5833 0.6250 NaN NaN 0.1267 NaN 0.1303 0.0760 NaN 0.9935 0.9128 NaN NaN 0.0345 0.5833 0.6250 NaN NaN 0.1238 NaN 0.1256 NaN block of flats 0.1224 NaN No 0.0 0.0 0.0 0.0 -441.0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 2.0 0.0
481 100555 0 Cash loans F N N 0 112500.000 337500.0 17230.5 337500.0 Unaccompanied Working Secondary / secondary special Single / not married Rented apartment 0.028663 -12293 -304 -81.0 -4476 NaN 1 1 0 1 0 0 Sales staff 1.0 2 2 FRIDAY 8 0 0 0 1 1 0 Trade: type 7 NaN 0.515845 0.529890 0.0227 NaN 0.9826 NaN NaN NaN 0.1034 0.0417 NaN NaN NaN 0.0194 NaN NaN 0.0231 NaN 0.9826 NaN NaN NaN 0.1034 0.0417 NaN NaN NaN 0.0202 NaN NaN 0.0229 NaN 0.9826 NaN NaN NaN 0.1034 0.0417 NaN NaN NaN 0.0198 NaN NaN NaN block of flats 0.0153 Stone, brick No 8.0 0.0 7.0 0.0 -438.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 0.0
482 100556 0 Cash loans F N Y 0 58500.000 50940.0 4959.0 45000.0 Unaccompanied Pensioner Secondary / secondary special Widow House / apartment 0.010500 -22735 365243 -1583.0 -4711 NaN 1 0 0 1 0 0 NaN 1.0 3 3 THURSDAY 10 0 0 0 0 0 0 XNA NaN 0.742158 NaN 0.0742 NaN 0.9851 NaN NaN NaN 0.0690 0.3333 NaN NaN NaN NaN NaN NaN 0.0756 NaN 0.9851 NaN NaN NaN 0.0690 0.3333 NaN NaN NaN NaN NaN NaN 0.0749 NaN 0.9851 NaN NaN NaN 0.0690 0.3333 NaN NaN NaN NaN NaN NaN NaN block of flats 0.0602 Stone, brick No 0.0 0.0 0.0 0.0 -1086.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NaN NaN NaN NaN NaN NaN
483 100557 0 Cash loans F Y Y 0 225000.000 1320975.0 43785.0 1183500.0 Unaccompanied Commercial associate Incomplete higher Married House / apartment 0.018801 -10825 -1802 -4757.0 -2238 11.0 1 1 0 1 1 0 Sales staff 2.0 2 2 MONDAY 10 0 0 0 0 0 0 Business Entity Type 3 0.610857 0.348836 0.631355 0.1113 0.0665 0.9836 0.7756 0.0680 0.1200 0.1034 0.3333 0.3750 0.1036 0.0908 0.1158 0.0000 0.0000 0.1134 0.0690 0.9836 0.7844 0.0686 0.1208 0.1034 0.3333 0.3750 0.1059 0.0992 0.1206 0.0000 0.0000 0.1124 0.0665 0.9836 0.7786 0.0685 0.12 0.1034 0.3333 0.3750 0.1054 0.0923 0.1179 0.0000 0.0000 reg oper account block of flats 0.1197 Panel No 0.0 0.0 0.0 0.0 -1266.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 1.0 2.0
484 100558 0 Revolving loans M N Y 0 157500.000 270000.0 13500.0 270000.0 Unaccompanied Working Secondary / secondary special Married House / apartment 0.030755 -19499 -1299 -7244.0 -3057 NaN 1 1 1 1 1 0 Laborers 2.0 2 2 TUESDAY 12 0 0 0 0 0 0 Business Entity Type 3 NaN 0.197612 0.098859 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 4.0 1.0 4.0 0.0 -211.0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 3.0
485 100559 0 Cash loans F Y Y 0 450000.000 2286211.5 116266.5 2182500.0 Unaccompanied State servant Higher education Married House / apartment 0.018850 -20589 -13994 -5893.0 -4106 7.0 1 1 0 1 0 0 Core staff 2.0 2 2 MONDAY 16 0 0 0 0 0 0 Security Ministries NaN 0.503266 NaN 0.1485 0.0861 0.9816 0.7484 0.0336 0.1600 0.1379 0.3333 0.3750 0.0893 0.1210 0.1553 0.0000 0.0000 0.1513 0.0894 0.9816 0.7583 0.0339 0.1611 0.1379 0.3333 0.3750 0.0913 0.1322 0.1618 0.0000 0.0000 0.1499 0.0861 0.9816 0.7518 0.0338 0.16 0.1379 0.3333 0.3750 0.0909 0.1231 0.1581 0.0000 0.0000 reg oper spec account block of flats 0.1405 Panel No 1.0 0.0 1.0 0.0 -2675.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NaN NaN NaN NaN NaN NaN
486 100560 0 Cash loans F N N 0 49500.000 225000.0 11488.5 225000.0 Unaccompanied Pensioner Higher education Married Municipal apartment 0.006305 -21157 365243 -8746.0 -4279 NaN 1 0 0 1 0 0 NaN 2.0 3 3 THURSDAY 11 0 0 0 0 0 0 XNA NaN 0.574461 0.252599 0.0361 0.0398 0.9617 0.4764 0.0061 0.0000 0.0690 0.1250 0.1667 0.0508 0.0294 0.0496 0.0000 0.0000 0.0368 0.0413 0.9618 0.4969 0.0061 0.0000 0.0690 0.1250 0.1667 0.0520 0.0321 0.0517 0.0000 0.0000 0.0364 0.0398 0.9617 0.4834 0.0061 0.00 0.0690 0.1250 0.1667 0.0517 0.0299 0.0505 0.0000 0.0000 reg oper account block of flats 0.0424 Stone, brick No 0.0 0.0 0.0 0.0 -961.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1.0 0.0 0.0 0.0 0.0 1.0
487 100562 0 Revolving loans M N Y 1 337500.000 675000.0 33750.0 675000.0 Unaccompanied State servant Higher education Married House / apartment 0.046220 -12232 -4332 -4130.0 -263 NaN 1 1 0 1 0 0 Managers 3.0 1 1 SATURDAY 11 0 0 0 0 0 0 Military 0.576783 0.031671 0.379100 0.0402 0.0000 0.9821 0.7552 0.0250 0.0000 0.0345 0.0417 0.0833 0.0144 0.0328 0.0224 0.0000 0.0000 0.0410 0.0000 0.9821 0.7648 0.0252 0.0000 0.0345 0.0417 0.0833 0.0147 0.0358 0.0233 0.0000 0.0000 0.0406 0.0000 0.9821 0.7585 0.0251 0.00 0.0345 0.0417 0.0833 0.0146 0.0333 0.0228 0.0000 0.0000 not specified block of flats 0.0267 Stone, brick No 0.0 0.0 0.0 0.0 -1530.0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 1.0 0.0 2.0
488 100563 0 Cash loans F N Y 0 135000.000 1175314.5 38974.5 1053000.0 Unaccompanied Working Secondary / secondary special Married House / apartment 0.006305 -15227 -1615 -5921.0 -3705 NaN 1 1 0 1 1 0 Sales staff 2.0 3 3 THURSDAY 6 0 0 0 0 0 0 Trade: type 7 0.439762 0.460477 0.529890 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 4.0 2.0 4.0 2.0 -298.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 1.0
489 100564 0 Cash loans M N Y 2 180000.000 835380.0 40320.0 675000.0 Unaccompanied Commercial associate Secondary / secondary special Married Municipal apartment 0.046220 -16304 -811 -8807.0 -4955 NaN 1 1 0 1 0 0 Drivers 4.0 1 1 WEDNESDAY 16 0 0 0 0 0 0 Government NaN 0.514376 0.486653 0.1227 0.0000 0.9776 0.6940 0.0003 0.0000 0.2759 0.1667 0.2083 0.0603 0.1000 0.1085 0.0000 0.0000 0.1250 0.0000 0.9777 0.7060 0.0003 0.0000 0.2759 0.1667 0.2083 0.0617 0.1093 0.1131 0.0000 0.0000 0.1239 0.0000 0.9776 0.6981 0.0003 0.00 0.2759 0.1667 0.2083 0.0613 0.1018 0.1105 0.0000 0.0000 org spec account block of flats 0.0855 Panel No 0.0 0.0 0.0 0.0 -2275.0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 0.0
490 100565 0 Cash loans F N Y 0 112500.000 518562.0 25078.5 463500.0 Family Pensioner Secondary / secondary special Married House / apartment 0.031329 -20934 365243 -1377.0 -4010 NaN 1 0 0 1 0 0 NaN 2.0 2 2 THURSDAY 10 0 0 0 0 0 0 XNA NaN 0.657316 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 0.0 0.0 0.0 0.0 -168.0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NaN NaN NaN NaN NaN NaN
491 100566 0 Cash loans F N Y 1 117000.000 932643.0 27400.5 778500.0 Unaccompanied Working Secondary / secondary special Married House / apartment 0.018029 -18125 -3443 -5118.0 -1680 NaN 1 1 0 1 0 0 Cooking staff 3.0 3 2 THURSDAY 10 0 0 0 0 0 0 Medicine 0.500328 0.726127 0.773896 NaN NaN 0.9752 NaN NaN NaN NaN NaN NaN NaN NaN 0.0080 NaN NaN NaN NaN 0.9752 NaN NaN NaN NaN NaN NaN NaN NaN 0.0083 NaN NaN NaN NaN 0.9752 NaN NaN NaN NaN NaN NaN NaN NaN 0.0081 NaN NaN NaN block of flats 0.0069 NaN Yes 0.0 0.0 0.0 0.0 -189.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 1.0 1.0
492 100567 1 Revolving loans M Y Y 0 99000.000 180000.0 9000.0 180000.0 Unaccompanied Working Secondary / secondary special Married House / apartment 0.028663 -8063 -261 -738.0 -735 10.0 1 1 0 1 0 0 Sales staff 2.0 2 2 MONDAY 12 0 0 0 0 0 0 Self-employed 0.129326 0.291660 0.217629 0.0289 0.0328 0.9727 0.6260 0.0056 0.0000 0.1724 0.0833 0.1250 0.0184 0.0235 0.0313 0.0000 0.0000 0.0294 0.0341 0.9727 0.6406 0.0056 0.0000 0.1724 0.0833 0.1250 0.0189 0.0257 0.0326 0.0000 0.0000 0.0291 0.0328 0.9727 0.6310 0.0056 0.00 0.1724 0.0833 0.1250 0.0188 0.0239 0.0319 0.0000 0.0000 reg oper account block of flats 0.0246 Block No 1.0 0.0 1.0 0.0 -390.0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 2.0 0.0
493 100570 0 Cash loans F Y N 0 180000.000 1395000.0 36927.0 1395000.0 Unaccompanied Commercial associate Secondary / secondary special Married House / apartment 0.028663 -13162 -215 -7286.0 -4794 5.0 1 1 0 1 0 0 Laborers 2.0 2 2 WEDNESDAY 15 0 0 0 0 0 0 Business Entity Type 3 NaN 0.569546 0.621226 0.1495 0.0934 0.9876 0.8300 0.0314 0.1600 0.1379 0.3333 0.3750 0.0772 0.1210 0.1551 0.0039 0.0006 0.1523 0.0969 0.9876 0.8367 0.0317 0.1611 0.1379 0.3333 0.3750 0.0789 0.1322 0.1616 0.0039 0.0007 0.1509 0.0934 0.9876 0.8323 0.0316 0.16 0.1379 0.3333 0.3750 0.0785 0.1231 0.1579 0.0039 0.0006 reg oper account block of flats 0.1393 Panel No 0.0 0.0 0.0 0.0 -19.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0.0 0.0 0.0 0.0 1.0 0.0
494 100571 0 Cash loans M Y Y 2 292500.000 983299.5 39127.5 904500.0 Unaccompanied Working Secondary / secondary special Married House / apartment 0.002042 -9970 -1912 -456.0 -2464 10.0 1 1 0 1 0 0 Drivers 4.0 3 3 WEDNESDAY 16 0 0 0 0 0 0 Transport: type 4 0.149511 0.132631 0.609276 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 1.0 0.0 1.0 0.0 0.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 1.0
495 100572 0 Cash loans F N Y 0 31500.000 314055.0 13437.0 238500.0 Unaccompanied Pensioner Lower secondary Married House / apartment 0.031329 -19110 365243 -9012.0 -2663 NaN 1 0 0 1 0 0 NaN 2.0 2 2 FRIDAY 9 0 0 0 0 0 0 XNA NaN 0.468946 0.713631 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 1.0 0.0 1.0 0.0 0.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 1.0
496 100573 0 Cash loans F Y N 1 157500.000 961146.0 26559.0 688500.0 Unaccompanied Working Secondary / secondary special Civil marriage House / apartment 0.015221 -13482 -1684 -2152.0 -2226 1.0 1 1 0 1 1 0 NaN 3.0 2 2 WEDNESDAY 13 0 0 0 0 0 0 Kindergarten 0.432729 0.568248 0.401407 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 4.0 0.0 3.0 0.0 -1478.0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 0.0
497 100574 0 Cash loans M Y Y 0 225000.000 1125000.0 60070.5 1125000.0 Unaccompanied Commercial associate Higher education Married House / apartment 0.026392 -18280 -241 -12213.0 -1817 4.0 1 1 1 1 0 0 Drivers 2.0 2 2 TUESDAY 14 0 0 0 0 0 0 Business Entity Type 3 NaN 0.717455 0.812823 0.1485 0.1115 0.9806 NaN NaN 0.1600 0.1379 0.3333 NaN NaN NaN 0.1585 NaN 0.0000 0.1513 0.1157 0.9806 NaN NaN 0.1611 0.1379 0.3333 NaN NaN NaN 0.1651 NaN 0.0000 0.1499 0.1115 0.9806 NaN NaN 0.16 0.1379 0.3333 NaN NaN NaN 0.1613 NaN 0.0000 NaN block of flats 0.1468 Panel No 2.0 0.0 2.0 0.0 -1417.0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 0.0 0.0
498 100575 0 Cash loans M Y Y 0 108000.000 247275.0 17586.0 225000.0 Unaccompanied Pensioner Higher education Civil marriage House / apartment 0.008230 -22443 365243 -1824.0 -4272 4.0 1 0 0 1 0 0 NaN 2.0 2 2 FRIDAY 14 0 0 0 0 0 0 XNA 0.853753 0.533703 0.785052 0.1845 0.1715 0.9975 NaN NaN 0.0000 0.4138 0.1667 NaN NaN NaN 0.1324 NaN 0.1653 0.1555 0.1780 0.9970 NaN NaN 0.0000 0.3448 0.1667 NaN NaN NaN 0.1380 NaN 0.1750 0.1863 0.1715 0.9975 NaN NaN 0.00 0.4138 0.1667 NaN NaN NaN 0.1348 NaN 0.1687 NaN block of flats 0.1694 Stone, brick No 1.0 0.0 1.0 0.0 -523.0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0 0.0 0.0 1.0 1.0
499 100576 0 Cash loans F N Y 2 180000.000 225000.0 24363.0 225000.0 Unaccompanied Commercial associate Secondary / secondary special Married House / apartment 0.032561 -12803 -342 -6902.0 -3485 NaN 1 1 0 1 0 0 Cooking staff 4.0 1 1 MONDAY 12 0 0 0 0 0 0 Other NaN 0.764189 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 3.0 0.0 3.0 0.0 -1220.0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 NaN NaN NaN NaN NaN NaN

500 rows × 122 columns

In [191]:
# Display the above data sample table, but with axes transposed and limited to 5 records, so that the table can 
# be included in the project's writeup.
display(application_train_data.head(n=5).transpose())
0 1 2 3 4
SK_ID_CURR 100002 100003 100004 100006 100007
TARGET 1 0 0 0 0
NAME_CONTRACT_TYPE Cash loans Cash loans Revolving loans Cash loans Cash loans
CODE_GENDER M F M F M
FLAG_OWN_CAR N N Y N N
FLAG_OWN_REALTY Y N Y Y Y
CNT_CHILDREN 0 0 0 0 0
AMT_INCOME_TOTAL 202500 270000 67500 135000 121500
AMT_CREDIT 406598 1.2935e+06 135000 312682 513000
AMT_ANNUITY 24700.5 35698.5 6750 29686.5 21865.5
AMT_GOODS_PRICE 351000 1.1295e+06 135000 297000 513000
NAME_TYPE_SUITE Unaccompanied Family Unaccompanied Unaccompanied Unaccompanied
NAME_INCOME_TYPE Working State servant Working Working Working
NAME_EDUCATION_TYPE Secondary / secondary special Higher education Secondary / secondary special Secondary / secondary special Secondary / secondary special
NAME_FAMILY_STATUS Single / not married Married Single / not married Civil marriage Single / not married
NAME_HOUSING_TYPE House / apartment House / apartment House / apartment House / apartment House / apartment
REGION_POPULATION_RELATIVE 0.018801 0.003541 0.010032 0.008019 0.028663
DAYS_BIRTH -9461 -16765 -19046 -19005 -19932
DAYS_EMPLOYED -637 -1188 -225 -3039 -3038
DAYS_REGISTRATION -3648 -1186 -4260 -9833 -4311
DAYS_ID_PUBLISH -2120 -291 -2531 -2437 -3458
OWN_CAR_AGE NaN NaN 26 NaN NaN
FLAG_MOBIL 1 1 1 1 1
FLAG_EMP_PHONE 1 1 1 1 1
FLAG_WORK_PHONE 0 0 1 0 0
FLAG_CONT_MOBILE 1 1 1 1 1
FLAG_PHONE 1 1 1 0 0
FLAG_EMAIL 0 0 0 0 0
OCCUPATION_TYPE Laborers Core staff Laborers Laborers Core staff
CNT_FAM_MEMBERS 1 2 1 2 1
REGION_RATING_CLIENT 2 1 2 2 2
REGION_RATING_CLIENT_W_CITY 2 1 2 2 2
WEEKDAY_APPR_PROCESS_START WEDNESDAY MONDAY MONDAY WEDNESDAY THURSDAY
HOUR_APPR_PROCESS_START 10 11 9 17 11
REG_REGION_NOT_LIVE_REGION 0 0 0 0 0
REG_REGION_NOT_WORK_REGION 0 0 0 0 0
LIVE_REGION_NOT_WORK_REGION 0 0 0 0 0
REG_CITY_NOT_LIVE_CITY 0 0 0 0 0
REG_CITY_NOT_WORK_CITY 0 0 0 0 1
LIVE_CITY_NOT_WORK_CITY 0 0 0 0 1
ORGANIZATION_TYPE Business Entity Type 3 School Government Business Entity Type 3 Religion
EXT_SOURCE_1 0.083037 0.311267 NaN NaN NaN
EXT_SOURCE_2 0.262949 0.622246 0.555912 0.650442 0.322738
EXT_SOURCE_3 0.139376 NaN 0.729567 NaN NaN
APARTMENTS_AVG 0.0247 0.0959 NaN NaN NaN
BASEMENTAREA_AVG 0.0369 0.0529 NaN NaN NaN
YEARS_BEGINEXPLUATATION_AVG 0.9722 0.9851 NaN NaN NaN
YEARS_BUILD_AVG 0.6192 0.796 NaN NaN NaN
COMMONAREA_AVG 0.0143 0.0605 NaN NaN NaN
ELEVATORS_AVG 0 0.08 NaN NaN NaN
ENTRANCES_AVG 0.069 0.0345 NaN NaN NaN
FLOORSMAX_AVG 0.0833 0.2917 NaN NaN NaN
FLOORSMIN_AVG 0.125 0.3333 NaN NaN NaN
LANDAREA_AVG 0.0369 0.013 NaN NaN NaN
LIVINGAPARTMENTS_AVG 0.0202 0.0773 NaN NaN NaN
LIVINGAREA_AVG 0.019 0.0549 NaN NaN NaN
NONLIVINGAPARTMENTS_AVG 0 0.0039 NaN NaN NaN
NONLIVINGAREA_AVG 0 0.0098 NaN NaN NaN
APARTMENTS_MODE 0.0252 0.0924 NaN NaN NaN
BASEMENTAREA_MODE 0.0383 0.0538 NaN NaN NaN
YEARS_BEGINEXPLUATATION_MODE 0.9722 0.9851 NaN NaN NaN
YEARS_BUILD_MODE 0.6341 0.804 NaN NaN NaN
COMMONAREA_MODE 0.0144 0.0497 NaN NaN NaN
ELEVATORS_MODE 0 0.0806 NaN NaN NaN
ENTRANCES_MODE 0.069 0.0345 NaN NaN NaN
FLOORSMAX_MODE 0.0833 0.2917 NaN NaN NaN
FLOORSMIN_MODE 0.125 0.3333 NaN NaN NaN
LANDAREA_MODE 0.0377 0.0128 NaN NaN NaN
LIVINGAPARTMENTS_MODE 0.022 0.079 NaN NaN NaN
LIVINGAREA_MODE 0.0198 0.0554 NaN NaN NaN
NONLIVINGAPARTMENTS_MODE 0 0 NaN NaN NaN
NONLIVINGAREA_MODE 0 0 NaN NaN NaN
APARTMENTS_MEDI 0.025 0.0968 NaN NaN NaN
BASEMENTAREA_MEDI 0.0369 0.0529 NaN NaN NaN
YEARS_BEGINEXPLUATATION_MEDI 0.9722 0.9851 NaN NaN NaN
YEARS_BUILD_MEDI 0.6243 0.7987 NaN NaN NaN
COMMONAREA_MEDI 0.0144 0.0608 NaN NaN NaN
ELEVATORS_MEDI 0 0.08 NaN NaN NaN
ENTRANCES_MEDI 0.069 0.0345 NaN NaN NaN
FLOORSMAX_MEDI 0.0833 0.2917 NaN NaN NaN
FLOORSMIN_MEDI 0.125 0.3333 NaN NaN NaN
LANDAREA_MEDI 0.0375 0.0132 NaN NaN NaN
LIVINGAPARTMENTS_MEDI 0.0205 0.0787 NaN NaN NaN
LIVINGAREA_MEDI 0.0193 0.0558 NaN NaN NaN
NONLIVINGAPARTMENTS_MEDI 0 0.0039 NaN NaN NaN
NONLIVINGAREA_MEDI 0 0.01 NaN NaN NaN
FONDKAPREMONT_MODE reg oper account reg oper account NaN NaN NaN
HOUSETYPE_MODE block of flats block of flats NaN NaN NaN
TOTALAREA_MODE 0.0149 0.0714 NaN NaN NaN
WALLSMATERIAL_MODE Stone, brick Block NaN NaN NaN
EMERGENCYSTATE_MODE No No NaN NaN NaN
OBS_30_CNT_SOCIAL_CIRCLE 2 1 0 2 0
DEF_30_CNT_SOCIAL_CIRCLE 2 0 0 0 0
OBS_60_CNT_SOCIAL_CIRCLE 2 1 0 2 0
DEF_60_CNT_SOCIAL_CIRCLE 2 0 0 0 0
DAYS_LAST_PHONE_CHANGE -1134 -828 -815 -617 -1106
FLAG_DOCUMENT_2 0 0 0 0 0
FLAG_DOCUMENT_3 1 1 0 1 0
FLAG_DOCUMENT_4 0 0 0 0 0
FLAG_DOCUMENT_5 0 0 0 0 0
FLAG_DOCUMENT_6 0 0 0 0 0
FLAG_DOCUMENT_7 0 0 0 0 0
FLAG_DOCUMENT_8 0 0 0 0 1
FLAG_DOCUMENT_9 0 0 0 0 0
FLAG_DOCUMENT_10 0 0 0 0 0
FLAG_DOCUMENT_11 0 0 0 0 0
FLAG_DOCUMENT_12 0 0 0 0 0
FLAG_DOCUMENT_13 0 0 0 0 0
FLAG_DOCUMENT_14 0 0 0 0 0
FLAG_DOCUMENT_15 0 0 0 0 0
FLAG_DOCUMENT_16 0 0 0 0 0
FLAG_DOCUMENT_17 0 0 0 0 0
FLAG_DOCUMENT_18 0 0 0 0 0
FLAG_DOCUMENT_19 0 0 0 0 0
FLAG_DOCUMENT_20 0 0 0 0 0
FLAG_DOCUMENT_21 0 0 0 0 0
AMT_REQ_CREDIT_BUREAU_HOUR 0 0 0 NaN 0
AMT_REQ_CREDIT_BUREAU_DAY 0 0 0 NaN 0
AMT_REQ_CREDIT_BUREAU_WEEK 0 0 0 NaN 0
AMT_REQ_CREDIT_BUREAU_MON 0 0 0 NaN 0
AMT_REQ_CREDIT_BUREAU_QRT 0 0 0 NaN 0
AMT_REQ_CREDIT_BUREAU_YEAR 1 0 0 NaN 0

122 rows × 5 columns

In [7]:
# Display a statistical description of the numerical features, along with all features that 
# have alredy been one-hot encoded, in the main (application) data table.
display(application_train_data.describe())
SK_ID_CURR TARGET CNT_CHILDREN AMT_INCOME_TOTAL AMT_CREDIT AMT_ANNUITY AMT_GOODS_PRICE REGION_POPULATION_RELATIVE DAYS_BIRTH DAYS_EMPLOYED DAYS_REGISTRATION DAYS_ID_PUBLISH OWN_CAR_AGE FLAG_MOBIL FLAG_EMP_PHONE FLAG_WORK_PHONE FLAG_CONT_MOBILE FLAG_PHONE FLAG_EMAIL CNT_FAM_MEMBERS REGION_RATING_CLIENT REGION_RATING_CLIENT_W_CITY HOUR_APPR_PROCESS_START REG_REGION_NOT_LIVE_REGION REG_REGION_NOT_WORK_REGION LIVE_REGION_NOT_WORK_REGION REG_CITY_NOT_LIVE_CITY REG_CITY_NOT_WORK_CITY LIVE_CITY_NOT_WORK_CITY EXT_SOURCE_1 EXT_SOURCE_2 EXT_SOURCE_3 APARTMENTS_AVG BASEMENTAREA_AVG YEARS_BEGINEXPLUATATION_AVG YEARS_BUILD_AVG COMMONAREA_AVG ELEVATORS_AVG ENTRANCES_AVG FLOORSMAX_AVG FLOORSMIN_AVG LANDAREA_AVG LIVINGAPARTMENTS_AVG LIVINGAREA_AVG NONLIVINGAPARTMENTS_AVG NONLIVINGAREA_AVG APARTMENTS_MODE BASEMENTAREA_MODE YEARS_BEGINEXPLUATATION_MODE YEARS_BUILD_MODE COMMONAREA_MODE ELEVATORS_MODE ENTRANCES_MODE FLOORSMAX_MODE FLOORSMIN_MODE LANDAREA_MODE LIVINGAPARTMENTS_MODE LIVINGAREA_MODE NONLIVINGAPARTMENTS_MODE NONLIVINGAREA_MODE APARTMENTS_MEDI BASEMENTAREA_MEDI YEARS_BEGINEXPLUATATION_MEDI YEARS_BUILD_MEDI COMMONAREA_MEDI ELEVATORS_MEDI ENTRANCES_MEDI FLOORSMAX_MEDI FLOORSMIN_MEDI LANDAREA_MEDI LIVINGAPARTMENTS_MEDI LIVINGAREA_MEDI NONLIVINGAPARTMENTS_MEDI NONLIVINGAREA_MEDI TOTALAREA_MODE OBS_30_CNT_SOCIAL_CIRCLE DEF_30_CNT_SOCIAL_CIRCLE OBS_60_CNT_SOCIAL_CIRCLE DEF_60_CNT_SOCIAL_CIRCLE DAYS_LAST_PHONE_CHANGE FLAG_DOCUMENT_2 FLAG_DOCUMENT_3 FLAG_DOCUMENT_4 FLAG_DOCUMENT_5 FLAG_DOCUMENT_6 FLAG_DOCUMENT_7 FLAG_DOCUMENT_8 FLAG_DOCUMENT_9 FLAG_DOCUMENT_10 FLAG_DOCUMENT_11 FLAG_DOCUMENT_12 FLAG_DOCUMENT_13 FLAG_DOCUMENT_14 FLAG_DOCUMENT_15 FLAG_DOCUMENT_16 FLAG_DOCUMENT_17 FLAG_DOCUMENT_18 FLAG_DOCUMENT_19 FLAG_DOCUMENT_20 FLAG_DOCUMENT_21 AMT_REQ_CREDIT_BUREAU_HOUR AMT_REQ_CREDIT_BUREAU_DAY AMT_REQ_CREDIT_BUREAU_WEEK AMT_REQ_CREDIT_BUREAU_MON AMT_REQ_CREDIT_BUREAU_QRT AMT_REQ_CREDIT_BUREAU_YEAR
count 307511.000000 307511.000000 307511.000000 3.075110e+05 3.075110e+05 307499.000000 3.072330e+05 307511.000000 307511.000000 307511.000000 307511.000000 307511.000000 104582.000000 307511.000000 307511.000000 307511.000000 307511.000000 307511.000000 307511.000000 307509.000000 307511.000000 307511.000000 307511.000000 307511.000000 307511.000000 307511.000000 307511.000000 307511.000000 307511.000000 134133.000000 3.068510e+05 246546.000000 151450.00000 127568.000000 157504.000000 103023.000000 92646.000000 143620.000000 152683.000000 154491.000000 98869.000000 124921.000000 97312.000000 153161.000000 93997.000000 137829.000000 151450.000000 127568.000000 157504.000000 103023.000000 92646.000000 143620.000000 152683.000000 154491.000000 98869.000000 124921.000000 97312.000000 153161.000000 93997.000000 137829.000000 151450.000000 127568.000000 157504.000000 103023.000000 92646.000000 143620.000000 152683.000000 154491.000000 98869.000000 124921.000000 97312.000000 153161.000000 93997.000000 137829.000000 159080.000000 306490.000000 306490.000000 306490.000000 306490.000000 307510.000000 307511.000000 307511.000000 307511.000000 307511.000000 307511.000000 307511.000000 307511.000000 307511.000000 307511.000000 307511.000000 307511.000000 307511.000000 307511.000000 307511.00000 307511.000000 307511.000000 307511.000000 307511.000000 307511.000000 307511.000000 265992.000000 265992.000000 265992.000000 265992.000000 265992.000000 265992.000000
mean 278180.518577 0.080729 0.417052 1.687979e+05 5.990260e+05 27108.573909 5.383962e+05 0.020868 -16036.995067 63815.045904 -4986.120328 -2994.202373 12.061091 0.999997 0.819889 0.199368 0.998133 0.281066 0.056720 2.152665 2.052463 2.031521 12.063419 0.015144 0.050769 0.040659 0.078173 0.230454 0.179555 0.502130 5.143927e-01 0.510853 0.11744 0.088442 0.977735 0.752471 0.044621 0.078942 0.149725 0.226282 0.231894 0.066333 0.100775 0.107399 0.008809 0.028358 0.114231 0.087543 0.977065 0.759637 0.042553 0.074490 0.145193 0.222315 0.228058 0.064958 0.105645 0.105975 0.008076 0.027022 0.117850 0.087955 0.977752 0.755746 0.044595 0.078078 0.149213 0.225897 0.231625 0.067169 0.101954 0.108607 0.008651 0.028236 0.102547 1.422245 0.143421 1.405292 0.100049 -962.858788 0.000042 0.710023 0.000081 0.015115 0.088055 0.000192 0.081376 0.003896 0.000023 0.003912 0.000007 0.003525 0.002936 0.00121 0.009928 0.000267 0.008130 0.000595 0.000507 0.000335 0.006402 0.007000 0.034362 0.267395 0.265474 1.899974
std 102790.175348 0.272419 0.722121 2.371231e+05 4.024908e+05 14493.737315 3.694465e+05 0.013831 4363.988632 141275.766519 3522.886321 1509.450419 11.944812 0.001803 0.384280 0.399526 0.043164 0.449521 0.231307 0.910682 0.509034 0.502737 3.265832 0.122126 0.219526 0.197499 0.268444 0.421124 0.383817 0.211062 1.910602e-01 0.194844 0.10824 0.082438 0.059223 0.113280 0.076036 0.134576 0.100049 0.144641 0.161380 0.081184 0.092576 0.110565 0.047732 0.069523 0.107936 0.084307 0.064575 0.110111 0.074445 0.132256 0.100977 0.143709 0.161160 0.081750 0.097880 0.111845 0.046276 0.070254 0.109076 0.082179 0.059897 0.112066 0.076144 0.134467 0.100368 0.145067 0.161934 0.082167 0.093642 0.112260 0.047415 0.070166 0.107462 2.400989 0.446698 2.379803 0.362291 826.808487 0.006502 0.453752 0.009016 0.122010 0.283376 0.013850 0.273412 0.062295 0.004771 0.062424 0.002550 0.059268 0.054110 0.03476 0.099144 0.016327 0.089798 0.024387 0.022518 0.018299 0.083849 0.110757 0.204685 0.916002 0.794056 1.869295
min 100002.000000 0.000000 0.000000 2.565000e+04 4.500000e+04 1615.500000 4.050000e+04 0.000290 -25229.000000 -17912.000000 -24672.000000 -7197.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 1.000000 1.000000 1.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.014568 8.173617e-08 0.000527 0.00000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 -4292.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.00000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
25% 189145.500000 0.000000 0.000000 1.125000e+05 2.700000e+05 16524.000000 2.385000e+05 0.010006 -19682.000000 -2760.000000 -7479.500000 -4299.000000 5.000000 1.000000 1.000000 0.000000 1.000000 0.000000 0.000000 2.000000 2.000000 2.000000 10.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.334007 3.924574e-01 0.370650 0.05770 0.044200 0.976700 0.687200 0.007800 0.000000 0.069000 0.166700 0.083300 0.018700 0.050400 0.045300 0.000000 0.000000 0.052500 0.040700 0.976700 0.699400 0.007200 0.000000 0.069000 0.166700 0.083300 0.016600 0.054200 0.042700 0.000000 0.000000 0.058300 0.043700 0.976700 0.691400 0.007900 0.000000 0.069000 0.166700 0.083300 0.018700 0.051300 0.045700 0.000000 0.000000 0.041200 0.000000 0.000000 0.000000 0.000000 -1570.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.00000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
50% 278202.000000 0.000000 0.000000 1.471500e+05 5.135310e+05 24903.000000 4.500000e+05 0.018850 -15750.000000 -1213.000000 -4504.000000 -3254.000000 9.000000 1.000000 1.000000 0.000000 1.000000 0.000000 0.000000 2.000000 2.000000 2.000000 12.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.505998 5.659614e-01 0.535276 0.08760 0.076300 0.981600 0.755200 0.021100 0.000000 0.137900 0.166700 0.208300 0.048100 0.075600 0.074500 0.000000 0.003600 0.084000 0.074600 0.981600 0.764800 0.019000 0.000000 0.137900 0.166700 0.208300 0.045800 0.077100 0.073100 0.000000 0.001100 0.086400 0.075800 0.981600 0.758500 0.020800 0.000000 0.137900 0.166700 0.208300 0.048700 0.076100 0.074900 0.000000 0.003100 0.068800 0.000000 0.000000 0.000000 0.000000 -757.000000 0.000000 1.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.00000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 1.000000
75% 367142.500000 0.000000 1.000000 2.025000e+05 8.086500e+05 34596.000000 6.795000e+05 0.028663 -12413.000000 -289.000000 -2010.000000 -1720.000000 15.000000 1.000000 1.000000 0.000000 1.000000 1.000000 0.000000 3.000000 2.000000 2.000000 14.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.675053 6.636171e-01 0.669057 0.14850 0.112200 0.986600 0.823200 0.051500 0.120000 0.206900 0.333300 0.375000 0.085600 0.121000 0.129900 0.003900 0.027700 0.143900 0.112400 0.986600 0.823600 0.049000 0.120800 0.206900 0.333300 0.375000 0.084100 0.131300 0.125200 0.003900 0.023100 0.148900 0.111600 0.986600 0.825600 0.051300 0.120000 0.206900 0.333300 0.375000 0.086800 0.123100 0.130300 0.003900 0.026600 0.127600 2.000000 0.000000 2.000000 0.000000 -274.000000 0.000000 1.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.00000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 3.000000
max 456255.000000 1.000000 19.000000 1.170000e+08 4.050000e+06 258025.500000 4.050000e+06 0.072508 -7489.000000 365243.000000 0.000000 0.000000 91.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 20.000000 3.000000 3.000000 23.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 0.962693 8.549997e-01 0.896010 1.00000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 348.000000 34.000000 344.000000 24.000000 0.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.00000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 4.000000 9.000000 8.000000 27.000000 261.000000 25.000000

8 rows × 106 columns

In [187]:
# Display the above statistical description table, but with axes inverted, so that the table can 
# be included in the project's writeup.
display(application_train_data.describe().transpose())
count mean std min 25% 50% 75% max
SK_ID_CURR 307511.0 278180.518577 102790.175348 1.000020e+05 189145.500000 278202.000000 367142.500000 4.562550e+05
TARGET 307511.0 0.080729 0.272419 0.000000e+00 0.000000 0.000000 0.000000 1.000000e+00
CNT_CHILDREN 307511.0 0.417052 0.722121 0.000000e+00 0.000000 0.000000 1.000000 1.900000e+01
AMT_INCOME_TOTAL 307511.0 168797.919297 237123.146279 2.565000e+04 112500.000000 147150.000000 202500.000000 1.170000e+08
AMT_CREDIT 307511.0 599025.999706 402490.776996 4.500000e+04 270000.000000 513531.000000 808650.000000 4.050000e+06
AMT_ANNUITY 307499.0 27108.573909 14493.737315 1.615500e+03 16524.000000 24903.000000 34596.000000 2.580255e+05
AMT_GOODS_PRICE 307233.0 538396.207429 369446.460540 4.050000e+04 238500.000000 450000.000000 679500.000000 4.050000e+06
REGION_POPULATION_RELATIVE 307511.0 0.020868 0.013831 2.900000e-04 0.010006 0.018850 0.028663 7.250800e-02
DAYS_BIRTH 307511.0 -16036.995067 4363.988632 -2.522900e+04 -19682.000000 -15750.000000 -12413.000000 -7.489000e+03
DAYS_EMPLOYED 307511.0 63815.045904 141275.766519 -1.791200e+04 -2760.000000 -1213.000000 -289.000000 3.652430e+05
DAYS_REGISTRATION 307511.0 -4986.120328 3522.886321 -2.467200e+04 -7479.500000 -4504.000000 -2010.000000 0.000000e+00
DAYS_ID_PUBLISH 307511.0 -2994.202373 1509.450419 -7.197000e+03 -4299.000000 -3254.000000 -1720.000000 0.000000e+00
OWN_CAR_AGE 104582.0 12.061091 11.944812 0.000000e+00 5.000000 9.000000 15.000000 9.100000e+01
FLAG_MOBIL 307511.0 0.999997 0.001803 0.000000e+00 1.000000 1.000000 1.000000 1.000000e+00
FLAG_EMP_PHONE 307511.0 0.819889 0.384280 0.000000e+00 1.000000 1.000000 1.000000 1.000000e+00
FLAG_WORK_PHONE 307511.0 0.199368 0.399526 0.000000e+00 0.000000 0.000000 0.000000 1.000000e+00
FLAG_CONT_MOBILE 307511.0 0.998133 0.043164 0.000000e+00 1.000000 1.000000 1.000000 1.000000e+00
FLAG_PHONE 307511.0 0.281066 0.449521 0.000000e+00 0.000000 0.000000 1.000000 1.000000e+00
FLAG_EMAIL 307511.0 0.056720 0.231307 0.000000e+00 0.000000 0.000000 0.000000 1.000000e+00
CNT_FAM_MEMBERS 307509.0 2.152665 0.910682 1.000000e+00 2.000000 2.000000 3.000000 2.000000e+01
REGION_RATING_CLIENT 307511.0 2.052463 0.509034 1.000000e+00 2.000000 2.000000 2.000000 3.000000e+00
REGION_RATING_CLIENT_W_CITY 307511.0 2.031521 0.502737 1.000000e+00 2.000000 2.000000 2.000000 3.000000e+00
HOUR_APPR_PROCESS_START 307511.0 12.063419 3.265832 0.000000e+00 10.000000 12.000000 14.000000 2.300000e+01
REG_REGION_NOT_LIVE_REGION 307511.0 0.015144 0.122126 0.000000e+00 0.000000 0.000000 0.000000 1.000000e+00
REG_REGION_NOT_WORK_REGION 307511.0 0.050769 0.219526 0.000000e+00 0.000000 0.000000 0.000000 1.000000e+00
LIVE_REGION_NOT_WORK_REGION 307511.0 0.040659 0.197499 0.000000e+00 0.000000 0.000000 0.000000 1.000000e+00
REG_CITY_NOT_LIVE_CITY 307511.0 0.078173 0.268444 0.000000e+00 0.000000 0.000000 0.000000 1.000000e+00
REG_CITY_NOT_WORK_CITY 307511.0 0.230454 0.421124 0.000000e+00 0.000000 0.000000 0.000000 1.000000e+00
LIVE_CITY_NOT_WORK_CITY 307511.0 0.179555 0.383817 0.000000e+00 0.000000 0.000000 0.000000 1.000000e+00
EXT_SOURCE_1 134133.0 0.502130 0.211062 1.456813e-02 0.334007 0.505998 0.675053 9.626928e-01
EXT_SOURCE_2 306851.0 0.514393 0.191060 8.173617e-08 0.392457 0.565961 0.663617 8.549997e-01
EXT_SOURCE_3 246546.0 0.510853 0.194844 5.272652e-04 0.370650 0.535276 0.669057 8.960095e-01
APARTMENTS_AVG 151450.0 0.117440 0.108240 0.000000e+00 0.057700 0.087600 0.148500 1.000000e+00
BASEMENTAREA_AVG 127568.0 0.088442 0.082438 0.000000e+00 0.044200 0.076300 0.112200 1.000000e+00
YEARS_BEGINEXPLUATATION_AVG 157504.0 0.977735 0.059223 0.000000e+00 0.976700 0.981600 0.986600 1.000000e+00
YEARS_BUILD_AVG 103023.0 0.752471 0.113280 0.000000e+00 0.687200 0.755200 0.823200 1.000000e+00
COMMONAREA_AVG 92646.0 0.044621 0.076036 0.000000e+00 0.007800 0.021100 0.051500 1.000000e+00
ELEVATORS_AVG 143620.0 0.078942 0.134576 0.000000e+00 0.000000 0.000000 0.120000 1.000000e+00
ENTRANCES_AVG 152683.0 0.149725 0.100049 0.000000e+00 0.069000 0.137900 0.206900 1.000000e+00
FLOORSMAX_AVG 154491.0 0.226282 0.144641 0.000000e+00 0.166700 0.166700 0.333300 1.000000e+00
FLOORSMIN_AVG 98869.0 0.231894 0.161380 0.000000e+00 0.083300 0.208300 0.375000 1.000000e+00
LANDAREA_AVG 124921.0 0.066333 0.081184 0.000000e+00 0.018700 0.048100 0.085600 1.000000e+00
LIVINGAPARTMENTS_AVG 97312.0 0.100775 0.092576 0.000000e+00 0.050400 0.075600 0.121000 1.000000e+00
LIVINGAREA_AVG 153161.0 0.107399 0.110565 0.000000e+00 0.045300 0.074500 0.129900 1.000000e+00
NONLIVINGAPARTMENTS_AVG 93997.0 0.008809 0.047732 0.000000e+00 0.000000 0.000000 0.003900 1.000000e+00
NONLIVINGAREA_AVG 137829.0 0.028358 0.069523 0.000000e+00 0.000000 0.003600 0.027700 1.000000e+00
APARTMENTS_MODE 151450.0 0.114231 0.107936 0.000000e+00 0.052500 0.084000 0.143900 1.000000e+00
BASEMENTAREA_MODE 127568.0 0.087543 0.084307 0.000000e+00 0.040700 0.074600 0.112400 1.000000e+00
YEARS_BEGINEXPLUATATION_MODE 157504.0 0.977065 0.064575 0.000000e+00 0.976700 0.981600 0.986600 1.000000e+00
YEARS_BUILD_MODE 103023.0 0.759637 0.110111 0.000000e+00 0.699400 0.764800 0.823600 1.000000e+00
COMMONAREA_MODE 92646.0 0.042553 0.074445 0.000000e+00 0.007200 0.019000 0.049000 1.000000e+00
ELEVATORS_MODE 143620.0 0.074490 0.132256 0.000000e+00 0.000000 0.000000 0.120800 1.000000e+00
ENTRANCES_MODE 152683.0 0.145193 0.100977 0.000000e+00 0.069000 0.137900 0.206900 1.000000e+00
FLOORSMAX_MODE 154491.0 0.222315 0.143709 0.000000e+00 0.166700 0.166700 0.333300 1.000000e+00
FLOORSMIN_MODE 98869.0 0.228058 0.161160 0.000000e+00 0.083300 0.208300 0.375000 1.000000e+00
LANDAREA_MODE 124921.0 0.064958 0.081750 0.000000e+00 0.016600 0.045800 0.084100 1.000000e+00
LIVINGAPARTMENTS_MODE 97312.0 0.105645 0.097880 0.000000e+00 0.054200 0.077100 0.131300 1.000000e+00
LIVINGAREA_MODE 153161.0 0.105975 0.111845 0.000000e+00 0.042700 0.073100 0.125200 1.000000e+00
NONLIVINGAPARTMENTS_MODE 93997.0 0.008076 0.046276 0.000000e+00 0.000000 0.000000 0.003900 1.000000e+00
NONLIVINGAREA_MODE 137829.0 0.027022 0.070254 0.000000e+00 0.000000 0.001100 0.023100 1.000000e+00
APARTMENTS_MEDI 151450.0 0.117850 0.109076 0.000000e+00 0.058300 0.086400 0.148900 1.000000e+00
BASEMENTAREA_MEDI 127568.0 0.087955 0.082179 0.000000e+00 0.043700 0.075800 0.111600 1.000000e+00
YEARS_BEGINEXPLUATATION_MEDI 157504.0 0.977752 0.059897 0.000000e+00 0.976700 0.981600 0.986600 1.000000e+00
YEARS_BUILD_MEDI 103023.0 0.755746 0.112066 0.000000e+00 0.691400 0.758500 0.825600 1.000000e+00
COMMONAREA_MEDI 92646.0 0.044595 0.076144 0.000000e+00 0.007900 0.020800 0.051300 1.000000e+00
ELEVATORS_MEDI 143620.0 0.078078 0.134467 0.000000e+00 0.000000 0.000000 0.120000 1.000000e+00
ENTRANCES_MEDI 152683.0 0.149213 0.100368 0.000000e+00 0.069000 0.137900 0.206900 1.000000e+00
FLOORSMAX_MEDI 154491.0 0.225897 0.145067 0.000000e+00 0.166700 0.166700 0.333300 1.000000e+00
FLOORSMIN_MEDI 98869.0 0.231625 0.161934 0.000000e+00 0.083300 0.208300 0.375000 1.000000e+00
LANDAREA_MEDI 124921.0 0.067169 0.082167 0.000000e+00 0.018700 0.048700 0.086800 1.000000e+00
LIVINGAPARTMENTS_MEDI 97312.0 0.101954 0.093642 0.000000e+00 0.051300 0.076100 0.123100 1.000000e+00
LIVINGAREA_MEDI 153161.0 0.108607 0.112260 0.000000e+00 0.045700 0.074900 0.130300 1.000000e+00
NONLIVINGAPARTMENTS_MEDI 93997.0 0.008651 0.047415 0.000000e+00 0.000000 0.000000 0.003900 1.000000e+00
NONLIVINGAREA_MEDI 137829.0 0.028236 0.070166 0.000000e+00 0.000000 0.003100 0.026600 1.000000e+00
TOTALAREA_MODE 159080.0 0.102547 0.107462 0.000000e+00 0.041200 0.068800 0.127600 1.000000e+00
OBS_30_CNT_SOCIAL_CIRCLE 306490.0 1.422245 2.400989 0.000000e+00 0.000000 0.000000 2.000000 3.480000e+02
DEF_30_CNT_SOCIAL_CIRCLE 306490.0 0.143421 0.446698 0.000000e+00 0.000000 0.000000 0.000000 3.400000e+01
OBS_60_CNT_SOCIAL_CIRCLE 306490.0 1.405292 2.379803 0.000000e+00 0.000000 0.000000 2.000000 3.440000e+02
DEF_60_CNT_SOCIAL_CIRCLE 306490.0 0.100049 0.362291 0.000000e+00 0.000000 0.000000 0.000000 2.400000e+01
DAYS_LAST_PHONE_CHANGE 307510.0 -962.858788 826.808487 -4.292000e+03 -1570.000000 -757.000000 -274.000000 0.000000e+00
FLAG_DOCUMENT_2 307511.0 0.000042 0.006502 0.000000e+00 0.000000 0.000000 0.000000 1.000000e+00
FLAG_DOCUMENT_3 307511.0 0.710023 0.453752 0.000000e+00 0.000000 1.000000 1.000000 1.000000e+00
FLAG_DOCUMENT_4 307511.0 0.000081 0.009016 0.000000e+00 0.000000 0.000000 0.000000 1.000000e+00
FLAG_DOCUMENT_5 307511.0 0.015115 0.122010 0.000000e+00 0.000000 0.000000 0.000000 1.000000e+00
FLAG_DOCUMENT_6 307511.0 0.088055 0.283376 0.000000e+00 0.000000 0.000000 0.000000 1.000000e+00
FLAG_DOCUMENT_7 307511.0 0.000192 0.013850 0.000000e+00 0.000000 0.000000 0.000000 1.000000e+00
FLAG_DOCUMENT_8 307511.0 0.081376 0.273412 0.000000e+00 0.000000 0.000000 0.000000 1.000000e+00
FLAG_DOCUMENT_9 307511.0 0.003896 0.062295 0.000000e+00 0.000000 0.000000 0.000000 1.000000e+00
FLAG_DOCUMENT_10 307511.0 0.000023 0.004771 0.000000e+00 0.000000 0.000000 0.000000 1.000000e+00
FLAG_DOCUMENT_11 307511.0 0.003912 0.062424 0.000000e+00 0.000000 0.000000 0.000000 1.000000e+00
FLAG_DOCUMENT_12 307511.0 0.000007 0.002550 0.000000e+00 0.000000 0.000000 0.000000 1.000000e+00
FLAG_DOCUMENT_13 307511.0 0.003525 0.059268 0.000000e+00 0.000000 0.000000 0.000000 1.000000e+00
FLAG_DOCUMENT_14 307511.0 0.002936 0.054110 0.000000e+00 0.000000 0.000000 0.000000 1.000000e+00
FLAG_DOCUMENT_15 307511.0 0.001210 0.034760 0.000000e+00 0.000000 0.000000 0.000000 1.000000e+00
FLAG_DOCUMENT_16 307511.0 0.009928 0.099144 0.000000e+00 0.000000 0.000000 0.000000 1.000000e+00
FLAG_DOCUMENT_17 307511.0 0.000267 0.016327 0.000000e+00 0.000000 0.000000 0.000000 1.000000e+00
FLAG_DOCUMENT_18 307511.0 0.008130 0.089798 0.000000e+00 0.000000 0.000000 0.000000 1.000000e+00
FLAG_DOCUMENT_19 307511.0 0.000595 0.024387 0.000000e+00 0.000000 0.000000 0.000000 1.000000e+00
FLAG_DOCUMENT_20 307511.0 0.000507 0.022518 0.000000e+00 0.000000 0.000000 0.000000 1.000000e+00
FLAG_DOCUMENT_21 307511.0 0.000335 0.018299 0.000000e+00 0.000000 0.000000 0.000000 1.000000e+00
AMT_REQ_CREDIT_BUREAU_HOUR 265992.0 0.006402 0.083849 0.000000e+00 0.000000 0.000000 0.000000 4.000000e+00
AMT_REQ_CREDIT_BUREAU_DAY 265992.0 0.007000 0.110757 0.000000e+00 0.000000 0.000000 0.000000 9.000000e+00
AMT_REQ_CREDIT_BUREAU_WEEK 265992.0 0.034362 0.204685 0.000000e+00 0.000000 0.000000 0.000000 8.000000e+00
AMT_REQ_CREDIT_BUREAU_MON 265992.0 0.267395 0.916002 0.000000e+00 0.000000 0.000000 0.000000 2.700000e+01
AMT_REQ_CREDIT_BUREAU_QRT 265992.0 0.265474 0.794056 0.000000e+00 0.000000 0.000000 0.000000 2.610000e+02
AMT_REQ_CREDIT_BUREAU_YEAR 265992.0 1.899974 1.869295 0.000000e+00 0.000000 1.000000 3.000000 2.500000e+01

106 rows × 8 columns

In [9]:
# Count number of delinquent repayers ('TARGET' value of 1) and non-delinquent repayers 
# ('TARGET' value of 0) in the training set of the main data table.

application_train_data['TARGET'].value_counts()
Out[9]:
0    282686
1     24825
Name: TARGET, Length: 2, dtype: int64
In [10]:
# Fraction of applicants in training set who were delinquent repayers. If you took a random 
# sample, this is the probability you would select a delinquent repayer by chance:
repayers_fraction = round(24825/(24825+282686), 4)
print('Fraction of training set who were delinquent payers: {}'.format(repayers_fraction))
Fraction of training set who were delinquent payers: 0.0807

Main Data Table Features with 'NaN' Entries

Do any features have mostly 'NaN' for their entries -- are any features too sparse to be of use?

In [24]:
# Get numerical counts of number of NaN entries in each column (feature) in the main data table.
features_sorted_by_NaN_count = application_train_data.isnull().sum().sort_values(ascending=False)

# Display only features with NaN counts greater than 0.
feature_NaN_counts = features_sorted_by_NaN_count[features_sorted_by_NaN_count > 0]

# Create a dataframe to summarize 'NaN' entries of features in the main data table (application_train_data)
feature_NaN_summary = pd.DataFrame(index=feature_NaN_counts.index, columns=['#_NaN_Entries','Fraction_of_entries_that_are_NaN','#_NaN_entries_who_are_Delinquent','Fraction_of_NaN_entries_who_are_Delinquent','#non_NaN_Entries','#non_NaN_entries_who_are_Delinquent','Fraction_of_non_NaN_entries_who_are_Delinquent'])

# Fill each row in the NaN summary dataframe
for feature_name in NaN_summary.index:
    # Get the amount and fraction of delinquents among all borrowers who 
    # have an 'NaN' entry for a particular feature. Do this for each feature that has at least 
    # one 'NaN' entry.
    number_of_NaN = feature_NaN_counts.loc[feature_name]
    feature_NaN_summary['#_NaN_Entries'][feature_name] = number_of_NaN
    number_delinquents_who_are_NaN = application_train_data[(application_train_data[feature_name].isnull()) & (application_train_data['TARGET'] == 1)].shape[0]
    feature_NaN_summary['#_NaN_entries_who_are_Delinquent'][feature_name] = number_delinquents_who_are_NaN
    fraction_of_NaN_entries_who_are_delinquents = round(number_delinquents_who_are_NaN/number_of_NaN,4)
    feature_NaN_summary['Fraction_of_NaN_entries_who_are_Delinquent'][feature_name] = fraction_of_NaN_entries_who_are_delinquents
    
    # Get the amount of non-'NaN' entries in each feature that 
    # has at least one 'NaN' entry.
    number_of_records = application_train_data[feature_name].shape[0]
    number_of_non_NaN = number_of_records - number_of_NaN
    feature_NaN_summary['#non_NaN_Entries'][feature_name] = number_of_non_NaN
    
    # Get the fraction of the total entries for a feature that are 'NaN'
    fraction_of_feature_entries_that_are_NaN = round(number_of_NaN/(number_of_NaN+number_of_non_NaN),4)
    feature_NaN_summary['Fraction_of_entries_that_are_NaN'][feature_name] = fraction_of_feature_entries_that_are_NaN
    
    # Get the amount and fraction of delinquents among all borrowers who 
    # have a non-'NaN' entry for a particular feature. Do this for each feature that has at least 
    # one 'NaN' entry.
    number_delinquents_who_are_not_NaN = application_train_data[(application_train_data[feature_name].notnull()) & (application_train_data['TARGET'] == 1)].shape[0]
    feature_NaN_summary['#non_NaN_entries_who_are_Delinquent'][feature_name] = number_delinquents_who_are_not_NaN
    fraction_of_non_NaN_entries_who_are_delinquents = round(number_delinquents_who_are_not_NaN/number_of_non_NaN,4)
    feature_NaN_summary['Fraction_of_non_NaN_entries_who_are_Delinquent'][feature_name] = fraction_of_NaN_entries_who_are_delinquents

# Display the NaN summary dataframe below
display(feature_NaN_summary, 'display.max_columns')
#_NaN_Entries Fraction_of_entries_that_are_NaN #_NaN_entries_who_are_Delinquent Fraction_of_NaN_entries_who_are_Delinquent #non_NaN_Entries #non_NaN_entries_who_are_Delinquent Fraction_of_non_NaN_entries_who_are_Delinquent
COMMONAREA_MEDI 214865 0.6987 18423 0.0857 92646 6402 0.0857
COMMONAREA_AVG 214865 0.6987 18423 0.0857 92646 6402 0.0857
COMMONAREA_MODE 214865 0.6987 18423 0.0857 92646 6402 0.0857
NONLIVINGAPARTMENTS_MODE 213514 0.6943 18327 0.0858 93997 6498 0.0858
NONLIVINGAPARTMENTS_MEDI 213514 0.6943 18327 0.0858 93997 6498 0.0858
NONLIVINGAPARTMENTS_AVG 213514 0.6943 18327 0.0858 93997 6498 0.0858
FONDKAPREMONT_MODE 210295 0.6839 18125 0.0862 97216 6700 0.0862
LIVINGAPARTMENTS_MEDI 210199 0.6835 18122 0.0862 97312 6703 0.0862
LIVINGAPARTMENTS_MODE 210199 0.6835 18122 0.0862 97312 6703 0.0862
LIVINGAPARTMENTS_AVG 210199 0.6835 18122 0.0862 97312 6703 0.0862
FLOORSMIN_MEDI 208642 0.6785 18008 0.0863 98869 6817 0.0863
FLOORSMIN_MODE 208642 0.6785 18008 0.0863 98869 6817 0.0863
FLOORSMIN_AVG 208642 0.6785 18008 0.0863 98869 6817 0.0863
YEARS_BUILD_MEDI 204488 0.665 17751 0.0868 103023 7074 0.0868
YEARS_BUILD_AVG 204488 0.665 17751 0.0868 103023 7074 0.0868
YEARS_BUILD_MODE 204488 0.665 17751 0.0868 103023 7074 0.0868
OWN_CAR_AGE 202929 0.6599 17249 0.085 104582 7576 0.085
LANDAREA_MODE 182590 0.5938 16104 0.0882 124921 8721 0.0882
LANDAREA_AVG 182590 0.5938 16104 0.0882 124921 8721 0.0882
LANDAREA_MEDI 182590 0.5938 16104 0.0882 124921 8721 0.0882
BASEMENTAREA_MEDI 179943 0.5852 16038 0.0891 127568 8787 0.0891
BASEMENTAREA_AVG 179943 0.5852 16038 0.0891 127568 8787 0.0891
BASEMENTAREA_MODE 179943 0.5852 16038 0.0891 127568 8787 0.0891
EXT_SOURCE_1 173378 0.5638 14771 0.0852 134133 10054 0.0852
NONLIVINGAREA_MEDI 169682 0.5518 15330 0.0903 137829 9495 0.0903
NONLIVINGAREA_AVG 169682 0.5518 15330 0.0903 137829 9495 0.0903
NONLIVINGAREA_MODE 169682 0.5518 15330 0.0903 137829 9495 0.0903
ELEVATORS_MODE 163891 0.533 14915 0.091 143620 9910 0.091
ELEVATORS_AVG 163891 0.533 14915 0.091 143620 9910 0.091
ELEVATORS_MEDI 163891 0.533 14915 0.091 143620 9910 0.091
WALLSMATERIAL_MODE 156341 0.5084 14271 0.0913 151170 10554 0.0913
APARTMENTS_MODE 156061 0.5075 14285 0.0915 151450 10540 0.0915
APARTMENTS_AVG 156061 0.5075 14285 0.0915 151450 10540 0.0915
APARTMENTS_MEDI 156061 0.5075 14285 0.0915 151450 10540 0.0915
ENTRANCES_MEDI 154828 0.5035 14211 0.0918 152683 10614 0.0918
ENTRANCES_MODE 154828 0.5035 14211 0.0918 152683 10614 0.0918
ENTRANCES_AVG 154828 0.5035 14211 0.0918 152683 10614 0.0918
LIVINGAREA_MEDI 154350 0.5019 14111 0.0914 153161 10714 0.0914
LIVINGAREA_MODE 154350 0.5019 14111 0.0914 153161 10714 0.0914
LIVINGAREA_AVG 154350 0.5019 14111 0.0914 153161 10714 0.0914
HOUSETYPE_MODE 154297 0.5018 14120 0.0915 153214 10705 0.0915
FLOORSMAX_MODE 153020 0.4976 14064 0.0919 154491 10761 0.0919
FLOORSMAX_MEDI 153020 0.4976 14064 0.0919 154491 10761 0.0919
FLOORSMAX_AVG 153020 0.4976 14064 0.0919 154491 10761 0.0919
YEARS_BEGINEXPLUATATION_MEDI 150007 0.4878 13808 0.092 157504 11017 0.092
YEARS_BEGINEXPLUATATION_AVG 150007 0.4878 13808 0.092 157504 11017 0.092
YEARS_BEGINEXPLUATATION_MODE 150007 0.4878 13808 0.092 157504 11017 0.092
TOTALAREA_MODE 148431 0.4827 13706 0.0923 159080 11119 0.0923
EMERGENCYSTATE_MODE 145755 0.474 13498 0.0926 161756 11327 0.0926
OCCUPATION_TYPE 96391 0.3135 6278 0.0651 211120 18547 0.0651
EXT_SOURCE_3 60965 0.1983 5677 0.0931 246546 19148 0.0931
AMT_REQ_CREDIT_BUREAU_QRT 41519 0.135 4292 0.1034 265992 20533 0.1034
AMT_REQ_CREDIT_BUREAU_YEAR 41519 0.135 4292 0.1034 265992 20533 0.1034
AMT_REQ_CREDIT_BUREAU_WEEK 41519 0.135 4292 0.1034 265992 20533 0.1034
AMT_REQ_CREDIT_BUREAU_MON 41519 0.135 4292 0.1034 265992 20533 0.1034
AMT_REQ_CREDIT_BUREAU_DAY 41519 0.135 4292 0.1034 265992 20533 0.1034
AMT_REQ_CREDIT_BUREAU_HOUR 41519 0.135 4292 0.1034 265992 20533 0.1034
NAME_TYPE_SUITE 1292 0.0042 70 0.0542 306219 24755 0.0542
OBS_30_CNT_SOCIAL_CIRCLE 1021 0.0033 36 0.0353 306490 24789 0.0353
OBS_60_CNT_SOCIAL_CIRCLE 1021 0.0033 36 0.0353 306490 24789 0.0353
DEF_60_CNT_SOCIAL_CIRCLE 1021 0.0033 36 0.0353 306490 24789 0.0353
DEF_30_CNT_SOCIAL_CIRCLE 1021 0.0033 36 0.0353 306490 24789 0.0353
EXT_SOURCE_2 660 0.0021 52 0.0788 306851 24773 0.0788
AMT_GOODS_PRICE 278 0.0009 21 0.0755 307233 24804 0.0755
AMT_ANNUITY 12 0 0 0 307499 24825 0
CNT_FAM_MEMBERS 2 0 0 0 307509 24825 0
DAYS_LAST_PHONE_CHANGE 1 0 0 0 307510 24825 0

67 rows × 7 columns

'display.max_columns'

The goal of creating the above summary of all 'NaN' entries in the main data table was to investigate three specific questions concerning these 67 features that each have at least one 'NaN' entry:

  1. Whether simply having 'NaN' for a particular feature could help predict whether a borrower was delinquent.
  2. Whether certain features with numerous 'NaN' entries would even be useful in predicting whether a borrower was delinquent. I am suspicious that a few of these features, such as COMMONAREA_MEDI, may have too few non-'NaN' entries that belong to delinquent payers. If too small a portion of the training set's target segment is included in these features, it's hard to see how they will be useful in making predictions that generalize to unseen datapoints.
  3. Do 'NaN' values primarily belong to numerical features, categorical features, or both?

In the case of question 1., I can confirm that for all 67 features, a borrower simply having an 'NaN' entry for a feature does not in any way shape or form predict whether the borrower will be more or less likely to be delinquent. I verified this for each feature by looking at the fractions of both its 'NaN' and non-'NaN' cohorts that were delinquents. For each feature, I found that the fraction of 'NaN' borrowers who were delinquent was identical to the fraction of non-'NaN' borrowers who were delinquent. If being 'NaN' for a particular feature were to have any chance of being a meaningful predictor of delinquency, I would have expected these two proportions to have had a stastically significant difference for that feature.

For question 2., it's helpful to remember that, as confirmed above, there are 24,825 borrowers in the training dataset who were delinquent repayers (who had a TARGET value of 1). Any feature that I retain in spite of its 'NaN' entries needs to still have a large enough amount of non-'NaN' entries that belong to delinquents. If the entire training set's delinquent population is not adequately represented among a particular feature's valid data points, it's unlikely that any predictions meaningfully informed by this feature would generalize well to unseen datapoints. The effective training set size for this feature would be just too small, and my model would be at risk of underfitting.

However, the big question is: what should the cutoff line be? What fraction of the 24,825 delinquent borrowers in the training set need to be captured by a feature's valid data points in order for the feature to have a chance of being useful in making predictions? There is no general rule of thumb that I can use to answer this for each of these features that has 'NaN' values. The short answer is, it depends -- on many factors such as distribution of the feature's valid data, as well as the type of classifier algorithm I'm using.

This is why for the time being I won't remove any of these features -- even features like COMMONAREA_MEDI that contain 'NaN' in over two-thirds of their entries. Instead of running detailed statistical analyses on these features' distributions, I will instead experiment with dimensionality reduction algorithms such as PCA and/or feature selection algorithms such as SelectKBest.

If a feature had had, say, 95% of its entries as 'NaN', I probably would have removed it from the dataset at this point. However, since the sparsest features in the main data table still have valid data in just over 30% of their entries, I don't want to prematurely remove a feature that may have a chance, however remote, of possibly contributing to useful predictions.

Finally, to answer question 3., I found that all but one of the 46 normalized numerical features contain 'NaN' values. About half of the 21 non-normalized numerical features contain 'NaN' entries. Only 3 out of the 15 categorical features that will need to be one-hot encoded contain 'NaN' entries. None of the already one-hot encoded categorical features contain 'NaN' entries.

Main Data Table Borrowers with 'NaN' Entries

Do any borrowers have mostly 'NaN' entries for their feature data -- could any datapoints representing borrowers be classified as outliers and removed because their feature data is too sparse?

In [43]:
# Get numerical counts of number of NaN entries in each row (borrower) in the main data table.
borrowers_sorted_by_NaN_count = application_train_data.isnull().sum(axis=1).sort_values(ascending=False)

# Display the number of 'NaN' entries for each borrower (left column 
# is borrower ID, right column is number of 'NaN' entries).
display(borrowers_sorted_by_NaN_count)
133770    61
244833    61
150206    61
69707     61
269786    61
269492    61
116937    61
185713    61
197736    61
87399     60
7077      60
115346    60
201306    60
146561    60
68013     60
53941     60
48839     60
36899     60
54457     60
241006    60
290495    60
127942    60
260125    60
109581    60
266184    60
172595    60
271341    60
258474    60
12087     60
193598    59
177876    59
249616    59
219410    59
74199     59
247473    59
18244     59
261487    59
148096    59
80085     59
53550     59
168835    59
198111    59
3498      59
236260    59
28769     59
286217    59
63325     59
213503    59
39841     59
45043     59
109165    59
215458    59
154667    59
62176     59
299853    59
303509    59
193060    58
50126     58
131669    58
277217    58
113159    58
254163    58
214296    58
49241     58
261513    58
256958    58
143851    58
224619    58
226725    58
26398     58
226384    58
116520    58
3718      58
277969    58
43073     58
3525      58
7730      58
254388    58
193578    58
187145    58
187120    58
41982     58
267335    58
114010    57
80680     57
178880    57
75436     57
204013    57
282990    57
46822     57
75397     57
112641    57
290491    57
107081    57
270250    57
259914    57
251174    57
223106    57
58989     57
44953     57
212556    57
193059    57
107773    57
170830    57
145534    57
267537    57
27137     57
125828    57
47391     57
193117    57
22753     57
304867    57
95501     57
155988    57
108760    57
245893    57
7287      57
104716    57
2036      57
108763    57
245532    57
63171     57
193875    57
241925    57
63037     57
222063    57
63053     57
295068    57
42186     57
164737    57
251189    57
238617    57
78542     57
24378     57
63039     57
162648    57
193283    57
303110    57
201301    57
63032     57
42179     57
102350    57
269931    57
212129    57
120684    57
222937    57
82420     57
280019    57
222933    57
193246    57
156094    57
108746    57
47333     57
234418    57
11886     57
47350     57
295043    57
278331    57
203405    57
267554    57
238628    57
6958      57
135812    57
262887    57
37825     57
47320     57
203957    57
120707    57
125859    57
75975     57
95395     57
56169     57
135800    57
178861    57
178848    57
234452    57
238621    57
142982    57
117745    57
234373    57
89954     57
19546     57
198215    57
198207    57
127559    57
14449     57
179087    57
95689     57
18568     57
238704    57
155804    57
160076    57
282912    57
217582    57
179055    57
164971    57
192869    57
37501     57
37513     57
140843    57
234305    57
112551    57
271189    57
298610    57
298609    57
298605    57
234284    57
95702     57
204031    57
304975    57
256507    57
204163    57
179160    57
128618    57
276469    57
296727    57
1333      57
192772    57
78638     57
18417     57
10430     57
175075    57
120853    57
125727    57
271354    57
44919     57
217563    57
301108    57
117720    57
155777    57
108819    57
127561    57
287816    57
290569    57
125729    57
21664     57
192901    57
15892     57
162580    57
234340    57
53885     57
223341    57
127514    57
198175    57
252525    57
80668     57
107758    57
223330    57
256563    57
214826    57
          ..
77356      0
241577     0
241614     0
77545      0
181695     0
181681     0
121620     0
241723     0
181674     0
14060      0
121635     0
77419      0
241799     0
20702      0
121643     0
121653     0
241139     0
181910     0
244113     0
240530     0
7546       0
121084     0
78411      0
25420      0
25421      0
121095     0
240512     0
121104     0
78233      0
182310     0
240595     0
182309     0
240598     0
78273      0
182308     0
121133     0
240451     0
121079     0
240439     0
240436     0
182464     0
78592      0
78576      0
121032     0
182449     0
182434     0
240281     0
78511      0
240330     0
121054     0
78497      0
240398     0
78472      0
240417     0
240423     0
25433      0
240679     0
181912     0
121301     0
182211     0
240948     0
240952     0
78011      0
20736      0
77991      0
182026     0
181969     0
78224      0
20733      0
25466      0
181952     0
77906      0
241065     0
241096     0
241100     0
182228     0
78074      0
78083      0
240866     0
240682     0
78189      0
7517       0
240731     0
78168      0
240757     0
78165      0
240771     0
240796     0
78139      0
182245     0
121224     0
121245     0
240848     0
7501       0
121712     0
121714     0
242031     0
243339     0
243242     0
180917     0
122106     0
243273     0
76182      0
25657      0
180896     0
180867     0
180815     0
76132      0
76122      0
122161     0
243503     0
243513     0
243528     0
243539     0
180927     0
243199     0
180940     0
243192     0
242967     0
76318      0
243020     0
243023     0
180993     0
243050     0
122073     0
20633      0
243103     0
14155      0
243125     0
76263      0
76242      0
243161     0
122091     0
243550     0
122250     0
77138      0
180572     0
243916     0
180609     0
75869      0
243964     0
75864      0
122329     0
122356     0
243999     0
243623     0
75803      0
180521     0
180498     0
75785      0
244068     0
20592      0
75736      0
75893      0
75912      0
180657     0
243885     0
243642     0
180790     0
122273     0
76026      0
243722     0
75976      0
180756     0
122300     0
243789     0
180748     0
75948      0
75934      0
243849     0
180682     0
243884     0
242965     0
181001     0
76341      0
181311     0
242355     0
242362     0
242371     0
242396     0
121861     0
76835      0
121874     0
242452     0
76356      0
181308     0
121892     0
76775      0
242482     0
76759      0
181290     0
242496     0
121847     0
76882      0
242318     0
76889      0
242066     0
77095      0
181445     0
77027      0
181429     0
181428     0
76985      0
20679      0
242161     0
242199     0
121800     0
121806     0
242233     0
181385     0
242294     0
242515     0
14116      0
121921     0
181113     0
242775     0
242785     0
122007     0
181088     0
76459      0
242838     0
181057     0
242874     0
242877     0
7261       0
76401      0
242905     0
20639      0
122033     0
122038     0
181112     0
76498      0
76728      0
76501      0
20654      0
76717      0
121946     0
181210     0
242560     0
181194     0
121951     0
181145     0
76621      0
242659     0
242676     0
76611      0
76606      0
181134     0
76597      0
153755     0
Length: 307511, dtype: int64
In [53]:
# Plot a histogram showing the number of borrowers that have a 
# certain amount of their feature data as 'NaN' entries.
plt.figure(figsize = (10,6))
plt.hist(borrowers_sorted_by_NaN_count)
plt.title('Borrowers Missing Main Data Table Features')
plt.xlabel('Number of Features Specified as \'NaN\'')
plt.ylabel('Number of Borrowers')
plt.savefig('borrowersnandata.png')
plt.show()

The above plot confirms that no borrowers are missing too many features such that they'd be considered outliers and would need to be removed from the trainingset.

At most, a borrower may be missing roughly only half of the main data table's 120 features. Just under 20,000 of the training dataset's 307,511 borrower records face this "worst-case" scenario. As it is, having 61 missing features falls well below the threshold at which I would decide to remove a borrower from the training dataset for having feature data that is too sparse. A borrower would have to be missing over 100 features, at least 5/6 of the featureset, for me to take the time to explore more deeply whether they may be an outlier.

Main Data Table Numerical and Categorical Feature Investigation: Unexpected Values

I explored samples from all features, as well as statistical descriptions of each numerical feature in the main data table (count, mean, standard deviation, minimum value, 25th percentile, 50th percentile, 75th percentile, and maximum value) to ensure that no features contained unexpected values that fall outside the range one would expect based on the feature's definition.

Examples of unexpected values include entries that are impossibly small/large, or values that are negative when only positive values would be expected.

I came across the following five anomalies:

  1. The following five numerical features indicate the number of days prior to the loan application's submission that a particular event took place:

    • DAYS_BIRTH
    • DAYS_EMPLOYED
    • DAYS_REGISTRATION
    • DAYS_ID_PUBLISH
    • DAYS_LAST_PHONE_CHANGE

    For example, DAYS_LAST_PHONE_CHANGE is defined by Home Credit as: "How many days before application did client change phone?"

    Values for the above five features are negative, which is expected since each value represents a point in time prior to the time of the loan application's submission, which Home Credit defines as time 0, which is the maximum value for a few of these features.

    What's unexpected is that the DAYS_EMPLOYED feature (the number of days the applicant has had a job prior to the day they submitted their loan application) has a maximum value that is both a positive number as well as unbelievably large. The maximum value for DAYS_EMPLOYED is 365,243 days, or just over 1,000 years.

    No human being lives for 1,000 years, let alone sustains a job for that long, so this entry clearly indicates that some sort of mistake was made. What's not yet clear to me is whether DAYS_EMPLOYED contains only one, a handful, or possibly several such points. Clearly this particular data point and any similar to it are outliers that should be removed, especially if the DAYS_EMPLOYED feature turns out to be otherwise useful for predicting target values. Indeed, based on my intuition, DAYS_EMPLOYED is one of the first features that I would guess would be relevant in predicting whether a borrower would eventually make a late loan payment.

    Creating and inspecting a histogram of this feature's data should help me to guage whether or not there are any other outlier data points that would need to be removed.

  2. I was undecided whether the features OWN_CAR_AGE, the age of the applicant's car, HOUR_APPR_PROCESS_START, the hour the loan application was submitted, CNT_CHILDREN, the number of children that the applicant has, and CNT_FAM_MEMBERS, the size of the applicant's family, should be thought of as categorical or numerical.

    The first reason for this uncertainty was because the entries in each feature were rounded to whole numbers. The second reason was that the range of whole number entries for each feature was quite limited -- [0,23] for HOUR_APPR_PROCESS_START, [0.0,91.0] for OWN_CAR_AGE, [0.0,19.0] for CNT_CHILDREN, and [1.0,20.0] for CNT_FAM_MEMBERS. Although the range of values in OWN_CAR_AGE is nearly four times that of HOUR_APPR_PROCESS_START, upon exploring the individual entries, I found that the majority of values appeared to be in the range [0.0,20.0]. This makes sense, considering that most cars don't last longer than twenty years. The efective range of HOUR_APPR_PROCESS_START values appeared to be even more narrow, with most entries concentrated inside the range [9,17]. This also makes sense, as regular business hours typically run from 9AM to 5PM.

    I ultimately decided that even though the effective ranges of both OWN_CAR_AGE and HOUR_APPR_PROCESS_START, are far more narrow than other numerical features in the main data table, the nature of each feature's data requires that I treat both as numerical features.

    The other categorical features in the main data table, such as whether the applicant owns a car, or the applicant's housing type, each have distinct entries that can encapsulate wildly different meanings and implications. The condition of owning a car is very different from the condition of not owning a car, and the lifestyle, financial and otherwise, of someone living in an apartment is likely very different from that of someone living in a stand-alone house.

    After thinking along these lines, it was easy for me to see that neither the entries in OWN_CAR_AGE nor those HOUR_APPR_PROCESS_START are necessarily always that different from one another in meaning or implication. For example, is submitting a loan application at 3PM really that different from submitting at 4PM? What about having a car that's eight years old versus having a car that's nine years old? For data like this, it is far more likely that there are meaningful sub-ranges, such as the afternoon hours of 1PM to 5PM, that may be helpful in predicting a borrower's target value. The only way I will be able to discover these sub-ranges is if I treat these features as numerical, not categorical.

    Things are different for the CNT_CHILDREN and CNT_FAM_MEMBERS features. Although the most children any loan applicant had was 19, at least 75% of all applicants had either zero children or just one child. I decided that it makes most sense to treat CNT_CHILDREN as a categorical feature, and re-engineer it to segment the borrower population into the following two categories: having no children, and having one or more children. Although I expect there will groups of borrowers of diminishing size that have 2,3,4,...,19 children, my hypothesis is that having no children versus having at least one child will be the information most useful to predict target values for the overall population of borrowers. Even if I were to spend time investigating the effects of having 2 vs. 3 vs. 4 vs. ... vs. 19 children, I know that my findings would apply to less than 25% of the overall population and any predictions informed by these effects likely wouldn't generalize well to unseen datapoints.

    I will transform the CNT_CHILDREN feature into a binary categorical feature called HAS_CHILDREN. If the value of CNT_CHILDREN is greater than 0, the value of HAS_CHILDREN will be 1. If the value of CNT_CHILDREN is 0, the value of HAS_CHILDREN will be 0.

    For CNT_FAM_MEMBERS, the situation is somewhat similar. 25% of borrowers in the training set have a family size of just one, 50% have a family of two or less, and 75% of borrowers have families of 3 people or less. I plan to re-engineer this feature to categorically segment borrowers into the following three groups: having a family size of one, a family size of two, and having a family that's three people or larger.

    I will transform the CNT_FAM_MEMBERS feature into a categorical feature called NUMBER_FAMILY_MEMBERS. If CNT_FAM_MEMBERS is 1.0, then the value of NUMBER_FAMILY_MEMBERS will be 'one'. If CNT_FAM_MEMBERS is 2.0, then NUMBER_FAMILY_MEMBERS will be 'two'. If CNT_FAM_MEMBERS is 3.0 or greater, then NUMBER_FAMILY_MEMBERS will be 'three_plus'. The new categorical feature NUMBER_FAMILY_MEMBERS will eventually be one-hot encoded.

  3. While most features reported as being normalized have a max value of 1.0 and a min value of 0.0. The following three features all have values within the range (0.0, 1.0), none of them have max values as 1.0, nor min values of 0.0. Even though Home Credit states that each of these three features have been normalized, because the max and min values of their range supposedly normalized values are different than what I've observed for all other features reported as normalized, I will need to pay special attention to the graphs of the distributions of these three features when I conduct my exploratory data visualization, in order to verify that these features' data is indeed distributed normally:

    • EXT_SOURCE_1
    • EXT_SOURCE_2
    • EXT_SOURCE_3
  4. The feature REGION_POPULATION_RELATIVE appears to be a unique case in that while it has also supposedly been normalized, its values merely fall into the range [0.000290, 0.072508]. All other features that Home Credit claims have been normalized more or less fall into the range [0.0,1.0]. It's therefore a given that I'll need to min-max scale this feature during data preprocessing.

  5. The following four features were defined by Home Credit as being "normalized":

    • FONDKAPREMONT_MODE
    • HOUSETYPE_MODE
    • WALLSMATERIAL_MODE
    • EMERGENCYSTATE_MODE

    Specifically, the definition contained in the HomeCredit_columns_description.csv file provided by Home Credit stated that these features were: "Normalized information about building where the client lives, What is average (_AVG suffix), modus (_MODE suffix), median (_MEDI suffix) apartment size, common area, living area, age of building, number of elevators, number of entrances, state of the building, number of floor"

    However, upon further investigation I found that each of these four features was in fact categorical, and would need to be one-hot encoded. WALLSMATERIAL_MODE, for example, contains entries such as "Stone, brick", "panel", and "block" that indicate the material(s) of which the walls in the borrower's house are built.

Main Data Table Numerical and Categorical Feature Summary

I divided the main data table's features into groups based both on whether the feature is categorical or numerical, as well as how the feature would need to be preprocessed. ie. has the feature already been normalized, has it already been one-hot encoded, etc. Features that contain at least one 'NaN' value are highlighted in red.

Categorical features needing one-hot encoding:

  1. NAME_CONTRACT_TYPE
  2. CODE_GENDER
  3. FLAG_OWN_CAR
  4. FLAG_OWN_REALTY
  5. NAME_INCOME_TYPE
  6. NAME_EDUCATION_TYPE
  7. NAME_FAMILY_STATUS
  8. NAME_HOUSING_TYPE
  9. REGION_RATING_CLIENT
  10. REGION_RATING_CLIENT_W_CITY
  11. WEEKDAY_APPR_PROCESS_START
  12. ORGANIZATION_TYPE
  13. NAME_TYPE_SUITE (1,292 'NaN' entries)
  14. OCCUPATION_TYPE (96,391 'NaN' entries)

Categorical features originally mis-identified by Home Credit as normalized (they also need to be one-hot encoded):

  1. EMERGENCYSTATE_MODE (145,755 'NaN' entries)
  2. HOUSETYPE_MODE (154,297 'NaN' entries)
  3. WALLSMATERIAL_MODE (156,341 'NaN' entries)
  4. FONDKAPREMONT_MODE (210,295 'NaN' entries)

Categorical features needing re-engineering:

  1. CNT_CHILDREN: Will be transformed into a binary categorical feature called HAS_CHILDREN, with value of 1 if borrower has 0 children vs. a value of 1 if borrower has at least one child.
  2. CNT_FAM_MEMBERS (2 'NaN' entries): Will be transformed into categorical feature called NUMBER_FAMILY_MEMBERS, with values of 'one', 'two', or 'three_plus' depending on whether the borrower's value for CNT_FAM_MEMBERS was 1, 2, or 3 or more. NUMBER_FAMILY_MEMBERS will eventually be one-hot encoded.

Binary Categorical features already one-hot encoded:

  1. FLAG_MOBIL
  2. FLAG_EMP_PHONE
  3. FLAG_WORK_PHONE
  4. FLAG_CONT_MOBILE
  5. FLAG_PHONE
  6. FLAG_EMAIL
  7. REG_REGION_NOT_LIVE_REGION
  8. REG_REGION_NOT_WORK_REGION
  9. LIVE_REGION_NOT_WORK_REGION
  10. REG_CITY_NOT_LIVE_CITY
  11. REG_CITY_NOT_WORK_CITY
  12. LIVE_CITY_NOT_WORK_CITY
  13. FLAG_DOCUMENT_2
  14. FLAG_DOCUMENT_3
  15. FLAG_DOCUMENT_4
  16. FLAG_DOCUMENT_5
  17. FLAG_DOCUMENT_6
  18. FLAG_DOCUMENT_7
  19. FLAG_DOCUMENT_8
  20. FLAG_DOCUMENT_9
  21. FLAG_DOCUMENT_10
  22. FLAG_DOCUMENT_11
  23. FLAG_DOCUMENT_12
  24. FLAG_DOCUMENT_13
  25. FLAG_DOCUMENT_14
  26. FLAG_DOCUMENT_15
  27. FLAG_DOCUMENT_16
  28. FLAG_DOCUMENT_17
  29. FLAG_DOCUMENT_18
  30. FLAG_DOCUMENT_19
  31. FLAG_DOCUMENT_20
  32. FLAG_DOCUMENT_21

Numerical features not identified as normalized, that have roughly normal distributions:

  1. DAYS_BIRTH
  2. DAYS_ID_PUBLISH
  3. HOUR_APPR_PROCESS_START

Numerical features not identified as normalized, that have skewed distributions:

  1. AMT_INCOME_TOTAL
  2. AMT_CREDIT
  3. AMT_ANNUITY (12 'NaN' entries)
  4. AMT_GOODS_PRICE (278 'NaN' entries)
  5. OBS_30_CNT_SOCIAL_CIRCLE (1,021 'NaN' entries)
  6. DEF_30_CNT_SOCIAL_CIRCLE (1,021 'NaN' entries)
  7. OBS_60_CNT_SOCIAL_CIRCLE (1,021 'NaN' entries)
  8. DEF_60_CNT_SOCIAL_CIRCLE (1,021 'NaN' entries)
  9. AMT_REQ_CREDIT_BUREAU_HOUR (41,519 'NaN' entries)
  10. AMT_REQ_CREDIT_BUREAU_DAY (41,519 'NaN' entries)
  11. AMT_REQ_CREDIT_BUREAU_WEEK (41,519 'NaN' entries)
  12. AMT_REQ_CREDIT_BUREAU_MON (41,519 'NaN' entries)
  13. AMT_REQ_CREDIT_BUREAU_QRT (41,519 'NaN' entries)
  14. AMT_REQ_CREDIT_BUREAU_YEAR (41,519 'NaN' entries)
  15. OWN_CAR_AGE (202,929 'NaN' entries)

Numerical features not identified as normalized, that have skewed distributions and negative values:

  1. DAYS_EMPLOYED: Will be transformed into a binary categorical feature called HAS_JOB, with value of 1 if borrower has a value of 0 or less for DAYS_EMPLOYED. HAS_JOB will have a value of 0 if the borrower is one of the 55,374 folks who has a value of 365243 for DAYS_EMPLOYED.
  2. DAYS_REGISTRATION
  3. DAYS_LAST_PHONE_CHANGE (1 'NaN' entry)

Numerical features identified as normalized, which are scaled to range [0,1]:

  1. EXT_SOURCE_2 (660 'NaN' entries)
  2. EXT_SOURCE_3 (60,965 'NaN' entries)
  3. TOTALAREA_MODE (148,431 'NaN' entries)
  4. YEARS_BEGINEXPLUATATION_AVG (150,007 'NaN' entries)
  5. YEARS_BEGINEXPLUATATION_MODE (150,007 'NaN' entries)
  6. YEARS_BEGINEXPLUATATION_MEDI (150,007 'NaN' entries)
  7. FLOORSMAX_AVG (153,020 'NaN' entries)
  8. FLOORSMAX_MODE (153,020 'NaN' entries)
  9. FLOORSMAX_MEDI (153,020 'NaN' entries)
  10. LIVINGAREA_AVG (154,350 'NaN' entries)
  11. LIVINGAREA_MODE (154,350 'NaN' entries)
  12. LIVINGAREA_MEDI (154,350 'NaN' entries)
  13. ENTRANCES_AVG (154,828 'NaN' entries)
  14. ENTRANCES_MODE (154,828 'NaN' entries)
  15. ENTRANCES_MEDI (154,828 'NaN' entries)
  16. APARTMENTS_AVG (156,061 'NaN' entries)
  17. APARTMENTS_MODE (156,061 'NaN' entries)
  18. APARTMENTS_MEDI (156,061 'NaN' entries)
  19. ELEVATORS_AVG (163,891 'NaN' entries)
  20. ELEVATORS_MODE (163,891 'NaN' entries)
  21. ELEVATORS_MEDI (163,891 'NaN' entries)
  22. NONLIVINGAREA_AVG (169,682 'NaN' entries)
  23. NONLIVINGAREA_MODE (169,682 'NaN' entries)
  24. NONLIVINGAREA_MEDI (169,682 'NaN' entries)
  25. EXT_SOURCE_1 (173,378 'NaN' entries)
  26. BASEMENTAREA_AVG (179,943 'NaN' entries)
  27. BASEMENTAREA_MODE (179,943 'NaN' entries)
  28. BASEMENTAREA_MEDI (179,943 'NaN' entries)
  29. LANDAREA_AVG (182,590 'NaN' entries)
  30. LANDAREA_MODE (182,590 'NaN' entries)
  31. LANDAREA_MEDI (182,590 'NaN' entries)
  32. YEARS_BUILD_AVG (204,488 'NaN' entries)
  33. YEARS_BUILD_MODE (204,488 'NaN' entries)
  34. YEARS_BUILD_MEDI (204,488 'NaN' entries)
  35. FLOORSMIN_AVG (208,642 'NaN' entries)
  36. FLOORSMIN_MODE (208,642 'NaN' entries)
  37. FLOORSMIN_MEDI (208,642 'NaN' entries)
  38. LIVINGAPARTMENTS_AVG (210,199 'NaN' entries)
  39. LIVINGAPARTMENTS_MODE (210,199 'NaN' entries)
  40. LIVINGAPARTMENTS_MEDI (210,199 'NaN' entries)
  41. NONLIVINGAPARTMENTS_AVG (213,514 'NaN' entries)
  42. NONLIVINGAPARTMENTS_MODE (213,514 'NaN' entries)
  43. NONLIVINGAPARTMENTS_MEDI (213,514 'NaN' entries)
  44. COMMONAREA_AVG (214,865 'NaN' entries)
  45. COMMONAREA_MODE (214,865 'NaN' entries)
  46. COMMONAREA_MEDI (214,865 'NaN' entries)

Numerical features identified as normalized, which are not scaled to range [0,1]:

  1. REGION_POPULATION_RELATIVE

Summing the above lists gives 14+4+2+32+3+15+3+46+1 = 120 features, which are all of the features in the main data table.

Main Data Table Featureset Definitions

  1. SK_ID_CURR: ID of loan in our sample
  2. TARGET: Target variable (1 - client with payment difficulties: he/she had late payment more than X days on at least one of the first Y installments of the loan in our sample, 0 - all other cases)
  3. NAME_CONTRACT_TYPE: Identification if loan is cash or revolving
  4. CODE_GENDER: Gender of the client
  5. FLAG_OWN_CAR: Flag if the client owns a car
  6. FLAG_OWN_REALTY: Flag if client owns a house or flat
  7. CNT_CHILDREN: Number of children the client has
  8. AMT_INCOME_TOTAL: Income of the client
  9. AMT_CREDIT: Credit amount of the loan
  10. AMT_ANNUITY: Loan annuity
  11. AMT_GOODS_PRICE: For consumer loans it is the price of the goods for which the loan is given
  12. NAME_TYPE_SUITE: Who was accompanying client when he was applying for the loan
  13. NAME_INCOME_TYPE: Clients income type (businessman, working, maternity leave,Ö)
  14. NAME_EDUCATION_TYPE: Level of highest education the client achieved
  15. NAME_FAMILY_STATUS: Family status of the client
  16. NAME_HOUSING_TYPE: What is the housing situation of the client (renting, living with parents, ...)
  17. REGION_POPULATION_RELATIVE: Normalized population of region where client lives (higher number means the client lives in more populated region) -- normalized
  18. DAYS_BIRTH: Client's age in days at the time of application -- time only relative to the application
  19. DAYS_EMPLOYED: How many days before the application the person started current employment -- time only relative to the application
  20. DAYS_REGISTRATION: How many days before the application did client change his registration -- time only relative to the application
  21. DAYS_ID_PUBLISH: How many days before the application did client change the identity document with which he applied for the loan -- time only relative to the application
  22. OWN_CAR_AGE: Age of client's car
  23. FLAG_MOBIL: Did client provide mobile phone (1=YES, 0=NO)
  24. FLAG_EMP_PHONE: Did client provide work phone (1=YES, 0=NO)
  25. FLAG_WORK_PHONE: Did client provide home phone (1=YES, 0=NO)
  26. FLAG_CONT_MOBILE: Was mobile phone reachable (1=YES, 0=NO)
  27. FLAG_PHONE: Did client provide home phone (1=YES, 0=NO)
  28. FLAG_EMAIL: Did client provide email (1=YES, 0=NO)
  29. OCCUPATION_TYPE: What kind of occupation does the client have
  30. CNT_FAM_MEMBERS: How many family members does client have
  31. REGION_RATING_CLIENT: Our rating of the region where client lives (1,2,3)
  32. REGION_RATING_CLIENT_W_CITY: Our rating of the region where client lives with taking city into account (1,2,3)
  33. WEEKDAY_APPR_PROCESS_START: On which day of the week did the client apply for the loan
  34. HOUR_APPR_PROCESS_START: Approximately at what hour did the client apply for the loan rounded
  35. REG_REGION_NOT_LIVE_REGION: Flag if client's permanent address does not match contact address (1=different, 0=same, at region level)
  36. REG_REGION_NOT_WORK_REGION: Flag if client's permanent address does not match work address (1=different, 0=same, at region level)
  37. LIVE_REGION_NOT_WORK_REGION: Flag if client's contact address does not match work address (1=different, 0=same, at region level)
  38. REG_CITY_NOT_LIVE_CITY: Flag if client's permanent address does not match contact address (1=different, 0=same, at city level)
  39. REG_CITY_NOT_WORK_CITY: Flag if client's permanent address does not match work address (1=different, 0=same, at city level)
  40. LIVE_CITY_NOT_WORK_CITY: Flag if client's contact address does not match work address (1=different, 0=same, at city level)
  41. ORGANIZATION_TYPE: Type of organization where client works
  42. EXT_SOURCE_1: Normalized score from external data source -- normalized
  43. EXT_SOURCE_2: Normalized score from external data source -- normalized
  44. EXT_SOURCE_3: Normalized score from external data source -- normalized
  45. APARTMENTS_AVG: Normalized information about building where the client lives, What is average (_AVG suffix), modus (_MODE suffix), median (_MEDI suffix) apartment size, common area, living area, age of building, number of elevators, number of entrances, state of the building, number of floor -- normalized
  46. BASEMENTAREA_AVG: Normalized information about building where the client lives, What is average (_AVG suffix), modus (_MODE suffix), median (_MEDI suffix) apartment size, common area, living area, age of building, number of elevators, number of entrances, state of the building, number of floor -- normalized
  47. YEARS_BEGINEXPLUATATION_AVG: Normalized information about building where the client lives, What is average (_AVG suffix), modus (_MODE suffix), median (_MEDI suffix) apartment size, common area, living area, age of building, number of elevators, number of entrances, state of the building, number of floor -- normalized
  48. YEARS_BUILD_AVG: Normalized information about building where the client lives, What is average (_AVG suffix), modus (_MODE suffix), median (_MEDI suffix) apartment size, common area, living area, age of building, number of elevators, number of entrances, state of the building, number of floor -- normalized
  49. COMMONAREA_AVG: Normalized information about building where the client lives, What is average (_AVG suffix), modus (_MODE suffix), median (_MEDI suffix) apartment size, common area, living area, age of building, number of elevators, number of entrances, state of the building, number of floor -- normalized
  50. ELEVATORS_AVG: Normalized information about building where the client lives, What is average (_AVG suffix), modus (_MODE suffix), median (_MEDI suffix) apartment size, common area, living area, age of building, number of elevators, number of entrances, state of the building, number of floor -- normalized
  51. ENTRANCES_AVG: Normalized information about building where the client lives, What is average (_AVG suffix), modus (_MODE suffix), median (_MEDI suffix) apartment size, common area, living area, age of building, number of elevators, number of entrances, state of the building, number of floor -- normalized
  52. FLOORSMAX_AVG: Normalized information about building where the client lives, What is average (_AVG suffix), modus (_MODE suffix), median (_MEDI suffix) apartment size, common area, living area, age of building, number of elevators, number of entrances, state of the building, number of floor -- normalized
  53. FLOORSMIN_AVG: Normalized information about building where the client lives, What is average (_AVG suffix), modus (_MODE suffix), median (_MEDI suffix) apartment size, common area, living area, age of building, number of elevators, number of entrances, state of the building, number of floor -- normalized
  54. LANDAREA_AVG: Normalized information about building where the client lives, What is average (_AVG suffix), modus (_MODE suffix), median (_MEDI suffix) apartment size, common area, living area, age of building, number of elevators, number of entrances, state of the building, number of floor -- normalized
  55. LIVINGAPARTMENTS_AVG: Normalized information about building where the client lives, What is average (_AVG suffix), modus (_MODE suffix), median (_MEDI suffix) apartment size, common area, living area, age of building, number of elevators, number of entrances, state of the building, number of floor -- normalized
  56. LIVINGAREA_AVG: Normalized information about building where the client lives, What is average (_AVG suffix), modus (_MODE suffix), median (_MEDI suffix) apartment size, common area, living area, age of building, number of elevators, number of entrances, state of the building, number of floor -- normalized
  57. NONLIVINGAPARTMENTS_AVG: Normalized information about building where the client lives, What is average (_AVG suffix), modus (_MODE suffix), median (_MEDI suffix) apartment size, common area, living area, age of building, number of elevators, number of entrances, state of the building, number of floor -- normalized
  58. NONLIVINGAREA_AVG: Normalized information about building where the client lives, What is average (_AVG suffix), modus (_MODE suffix), median (_MEDI suffix) apartment size, common area, living area, age of building, number of elevators, number of entrances, state of the building, number of floor -- normalized
  59. APARTMENTS_MODE: Normalized information about building where the client lives, What is average (_AVG suffix), modus (_MODE suffix), median (_MEDI suffix) apartment size, common area, living area, age of building, number of elevators, number of entrances, state of the building, number of floor -- normalized
  60. BASEMENTAREA_MODE: Normalized information about building where the client lives, What is average (_AVG suffix), modus (_MODE suffix), median (_MEDI suffix) apartment size, common area, living area, age of building, number of elevators, number of entrances, state of the building, number of floor -- normalized
  61. YEARS_BEGINEXPLUATATION_MODE: Normalized information about building where the client lives, What is average (_AVG suffix), modus (_MODE suffix), median (_MEDI suffix) apartment size, common area, living area, age of building, number of elevators, number of entrances, state of the building, number of floor -- normalized
  62. YEARS_BUILD_MODE: Normalized information about building where the client lives, What is average (_AVG suffix), modus (_MODE suffix), median (_MEDI suffix) apartment size, common area, living area, age of building, number of elevators, number of entrances, state of the building, number of floor -- normalized
  63. COMMONAREA_MODE: Normalized information about building where the client lives, What is average (_AVG suffix), modus (_MODE suffix), median (_MEDI suffix) apartment size, common area, living area, age of building, number of elevators, number of entrances, state of the building, number of floor -- normalized
  64. ELEVATORS_MODE: Normalized information about building where the client lives, What is average (_AVG suffix), modus (_MODE suffix), median (_MEDI suffix) apartment size, common area, living area, age of building, number of elevators, number of entrances, state of the building, number of floor -- normalized
  65. ENTRANCES_MODE: Normalized information about building where the client lives, What is average (_AVG suffix), modus (_MODE suffix), median (_MEDI suffix) apartment size, common area, living area, age of building, number of elevators, number of entrances, state of the building, number of floor -- normalized
  66. FLOORSMAX_MODE: Normalized information about building where the client lives, What is average (_AVG suffix), modus (_MODE suffix), median (_MEDI suffix) apartment size, common area, living area, age of building, number of elevators, number of entrances, state of the building, number of floor -- normalized
  67. FLOORSMIN_MODE: Normalized information about building where the client lives, What is average (_AVG suffix), modus (_MODE suffix), median (_MEDI suffix) apartment size, common area, living area, age of building, number of elevators, number of entrances, state of the building, number of floor -- normalized
  68. LANDAREA_MODE: Normalized information about building where the client lives, What is average (_AVG suffix), modus (_MODE suffix), median (_MEDI suffix) apartment size, common area, living area, age of building, number of elevators, number of entrances, state of the building, number of floor -- normalized
  69. LIVINGAPARTMENTS_MODE: Normalized information about building where the client lives, What is average (_AVG suffix), modus (_MODE suffix), median (_MEDI suffix) apartment size, common area, living area, age of building, number of elevators, number of entrances, state of the building, number of floor -- normalized
  70. LIVINGAREA_MODE: Normalized information about building where the client lives, What is average (_AVG suffix), modus (_MODE suffix), median (_MEDI suffix) apartment size, common area, living area, age of building, number of elevators, number of entrances, state of the building, number of floor -- normalized
  71. NONLIVINGAPARTMENTS_MODE: Normalized information about building where the client lives, What is average (_AVG suffix), modus (_MODE suffix), median (_MEDI suffix) apartment size, common area, living area, age of building, number of elevators, number of entrances, state of the building, number of floor -- normalized
  72. NONLIVINGAREA_MODE: Normalized information about building where the client lives, What is average (_AVG suffix), modus (_MODE suffix), median (_MEDI suffix) apartment size, common area, living area, age of building, number of elevators, number of entrances, state of the building, number of floor -- normalized
  73. APARTMENTS_MEDI: Normalized information about building where the client lives, What is average (_AVG suffix), modus (_MODE suffix), median (_MEDI suffix) apartment size, common area, living area, age of building, number of elevators, number of entrances, state of the building, number of floor -- normalized
  74. BASEMENTAREA_MEDI: Normalized information about building where the client lives, What is average (_AVG suffix), modus (_MODE suffix), median (_MEDI suffix) apartment size, common area, living area, age of building, number of elevators, number of entrances, state of the building, number of floor -- normalized
  75. YEARS_BEGINEXPLUATATION_MEDI: Normalized information about building where the client lives, What is average (_AVG suffix), modus (_MODE suffix), median (_MEDI suffix) apartment size, common area, living area, age of building, number of elevators, number of entrances, state of the building, number of floor -- normalized
  76. YEARS_BUILD_MEDI: Normalized information about building where the client lives, What is average (_AVG suffix), modus (_MODE suffix), median (_MEDI suffix) apartment size, common area, living area, age of building, number of elevators, number of entrances, state of the building, number of floor -- normalized
  77. COMMONAREA_MEDI: Normalized information about building where the client lives, What is average (_AVG suffix), modus (_MODE suffix), median (_MEDI suffix) apartment size, common area, living area, age of building, number of elevators, number of entrances, state of the building, number of floor -- normalized
  78. ELEVATORS_MEDI: Normalized information about building where the client lives, What is average (_AVG suffix), modus (_MODE suffix), median (_MEDI suffix) apartment size, common area, living area, age of building, number of elevators, number of entrances, state of the building, number of floor -- normalized
  79. ENTRANCES_MEDI: Normalized information about building where the client lives, What is average (_AVG suffix), modus (_MODE suffix), median (_MEDI suffix) apartment size, common area, living area, age of building, number of elevators, number of entrances, state of the building, number of floor -- normalized
  80. FLOORSMAX_MEDI: Normalized information about building where the client lives, What is average (_AVG suffix), modus (_MODE suffix), median (_MEDI suffix) apartment size, common area, living area, age of building, number of elevators, number of entrances, state of the building, number of floor -- normalized
  81. FLOORSMIN_MEDI: Normalized information about building where the client lives, What is average (_AVG suffix), modus (_MODE suffix), median (_MEDI suffix) apartment size, common area, living area, age of building, number of elevators, number of entrances, state of the building, number of floor -- normalized
  82. LANDAREA_MEDI: Normalized information about building where the client lives, What is average (_AVG suffix), modus (_MODE suffix), median (_MEDI suffix) apartment size, common area, living area, age of building, number of elevators, number of entrances, state of the building, number of floor -- normalized
  83. LIVINGAPARTMENTS_MEDI: Normalized information about building where the client lives, What is average (_AVG suffix), modus (_MODE suffix), median (_MEDI suffix) apartment size, common area, living area, age of building, number of elevators, number of entrances, state of the building, number of floor -- normalized
  84. LIVINGAREA_MEDI: Normalized information about building where the client lives, What is average (_AVG suffix), modus (_MODE suffix), median (_MEDI suffix) apartment size, common area, living area, age of building, number of elevators, number of entrances, state of the building, number of floor -- normalized
  85. NONLIVINGAPARTMENTS_MEDI: Normalized information about building where the client lives, What is average (_AVG suffix), modus (_MODE suffix), median (_MEDI suffix) apartment size, common area, living area, age of building, number of elevators, number of entrances, state of the building, number of floor -- normalized
  86. NONLIVINGAREA_MEDI: Normalized information about building where the client lives, What is average (_AVG suffix), modus (_MODE suffix), median (_MEDI suffix) apartment size, common area, living area, age of building, number of elevators, number of entrances, state of the building, number of floor -- normalized
  87. FONDKAPREMONT_MODE: Normalized information about building where the client lives, What is average (_AVG suffix), modus (_MODE suffix), median (_MEDI suffix) apartment size, common area, living area, age of building, number of elevators, number of entrances, state of the building, number of floor -- normalized
  88. HOUSETYPE_MODE: Normalized information about building where the client lives, What is average (_AVG suffix), modus (_MODE suffix), median (_MEDI suffix) apartment size, common area, living area, age of building, number of elevators, number of entrances, state of the building, number of floor -- normalized
  89. TOTALAREA_MODE: Normalized information about building where the client lives, What is average (_AVG suffix), modus (_MODE suffix), median (_MEDI suffix) apartment size, common area, living area, age of building, number of elevators, number of entrances, state of the building, number of floor -- normalized
  90. WALLSMATERIAL_MODE: Normalized information about building where the client lives, What is average (_AVG suffix), modus (_MODE suffix), median (_MEDI suffix) apartment size, common area, living area, age of building, number of elevators, number of entrances, state of the building, number of floor -- normalized
  91. EMERGENCYSTATE_MODE: Normalized information about building where the client lives, What is average (_AVG suffix), modus (_MODE suffix), median (_MEDI suffix) apartment size, common area, living area, age of building, number of elevators, number of entrances, state of the building, number of floor -- normalized
  92. OBS_30_CNT_SOCIAL_CIRCLE: How many observation of client's social surroundings with observable 30 DPD (days past due) default
  93. DEF_30_CNT_SOCIAL_CIRCLE: How many observation of client's social surroundings defaulted on 30 DPD (days past due)
  94. OBS_60_CNT_SOCIAL_CIRCLE: How many observation of client's social surroundings with observable 60 DPD (days past due) default
  95. DEF_60_CNT_SOCIAL_CIRCLE: How many observation of client's social surroundings defaulted on 60 (days past due) DPD
  96. DAYS_LAST_PHONE_CHANGE: How many days before application did client change phone
  97. FLAG_DOCUMENT_2: Did client provide document 2
  98. FLAG_DOCUMENT_3: Did client provide document 3
  99. FLAG_DOCUMENT_4: Did client provide document 4
  100. FLAG_DOCUMENT_5: Did client provide document 5
  101. FLAG_DOCUMENT_6: Did client provide document 6
  102. FLAG_DOCUMENT_7: Did client provide document 7
  103. FLAG_DOCUMENT_8: Did client provide document 8
  104. FLAG_DOCUMENT_9: Did client provide document 9
  105. FLAG_DOCUMENT_10: Did client provide document 10
  106. FLAG_DOCUMENT_11: Did client provide document 11
  107. FLAG_DOCUMENT_12: Did client provide document 12
  108. FLAG_DOCUMENT_13: Did client provide document 13
  109. FLAG_DOCUMENT_14: Did client provide document 14
  110. FLAG_DOCUMENT_15: Did client provide document 15
  111. FLAG_DOCUMENT_16: Did client provide document 16
  112. FLAG_DOCUMENT_17: Did client provide document 17
  113. FLAG_DOCUMENT_18: Did client provide document 18
  114. FLAG_DOCUMENT_19: Did client provide document 19
  115. FLAG_DOCUMENT_20: Did client provide document 20
  116. FLAG_DOCUMENT_21: Did client provide document 21
  117. AMT_REQ_CREDIT_BUREAU_HOUR: Number of enquiries to Credit Bureau about the client one hour before application
  118. AMT_REQ_CREDIT_BUREAU_DAY: Number of enquiries to Credit Bureau about the client one day before application (excluding one hour before application)
  119. AMT_REQ_CREDIT_BUREAU_WEEK: Number of enquiries to Credit Bureau about the client one week before application (excluding one day before application)
  120. AMT_REQ_CREDIT_BUREAU_MON: Number of enquiries to Credit Bureau about the client one month before application (excluding one week before application)
  121. AMT_REQ_CREDIT_BUREAU_QRT: Number of enquiries to Credit Bureau about the client 3 month before application (excluding one month before application)
  122. AMT_REQ_CREDIT_BUREAU_YEAR: Number of enquiries to Credit Bureau about the client one day year (excluding last 3 months before application)

2. Bureau Data Table (bureau.csv)

In [188]:
# Display the first five records
display(bureau_data.head(n=5))
SK_ID_CURR SK_ID_BUREAU CREDIT_ACTIVE CREDIT_CURRENCY DAYS_CREDIT CREDIT_DAY_OVERDUE DAYS_CREDIT_ENDDATE DAYS_ENDDATE_FACT AMT_CREDIT_MAX_OVERDUE CNT_CREDIT_PROLONG AMT_CREDIT_SUM AMT_CREDIT_SUM_DEBT AMT_CREDIT_SUM_LIMIT AMT_CREDIT_SUM_OVERDUE CREDIT_TYPE DAYS_CREDIT_UPDATE AMT_ANNUITY
0 215354 5714462 Closed currency 1 -497 0 -153.0 -153.0 NaN 0 91323.0 0.0 NaN 0.0 Consumer credit -131 NaN
1 215354 5714463 Active currency 1 -208 0 1075.0 NaN NaN 0 225000.0 171342.0 NaN 0.0 Credit card -20 NaN
2 215354 5714464 Active currency 1 -203 0 528.0 NaN NaN 0 464323.5 NaN NaN 0.0 Consumer credit -16 NaN
3 215354 5714465 Active currency 1 -203 0 NaN NaN NaN 0 90000.0 NaN NaN 0.0 Credit card -16 NaN
4 215354 5714466 Active currency 1 -629 0 1197.0 NaN 77674.5 0 2700000.0 NaN NaN 0.0 Consumer credit -21 NaN

5 rows × 17 columns

In [190]:
# Display the above data sample table, but with axes transposed, so that the table can 
# be included in the project's writeup.
display(bureau_data.head(n=5).transpose())
0 1 2 3 4
SK_ID_CURR 215354 215354 215354 215354 215354
SK_ID_BUREAU 5714462 5714463 5714464 5714465 5714466
CREDIT_ACTIVE Closed Active Active Active Active
CREDIT_CURRENCY currency 1 currency 1 currency 1 currency 1 currency 1
DAYS_CREDIT -497 -208 -203 -203 -629
CREDIT_DAY_OVERDUE 0 0 0 0 0
DAYS_CREDIT_ENDDATE -153 1075 528 NaN 1197
DAYS_ENDDATE_FACT -153 NaN NaN NaN NaN
AMT_CREDIT_MAX_OVERDUE NaN NaN NaN NaN 77674.5
CNT_CREDIT_PROLONG 0 0 0 0 0
AMT_CREDIT_SUM 91323 225000 464324 90000 2.7e+06
AMT_CREDIT_SUM_DEBT 0 171342 NaN NaN NaN
AMT_CREDIT_SUM_LIMIT NaN NaN NaN NaN NaN
AMT_CREDIT_SUM_OVERDUE 0 0 0 0 0
CREDIT_TYPE Consumer credit Credit card Consumer credit Credit card Consumer credit
DAYS_CREDIT_UPDATE -131 -20 -16 -16 -21
AMT_ANNUITY NaN NaN NaN NaN NaN

17 rows × 5 columns

In [198]:
# Display a statistical description of the numerical features in the bureau data table.
display(bureau_data.describe())
SK_ID_CURR SK_ID_BUREAU DAYS_CREDIT CREDIT_DAY_OVERDUE DAYS_CREDIT_ENDDATE DAYS_ENDDATE_FACT AMT_CREDIT_MAX_OVERDUE CNT_CREDIT_PROLONG AMT_CREDIT_SUM AMT_CREDIT_SUM_DEBT AMT_CREDIT_SUM_LIMIT AMT_CREDIT_SUM_OVERDUE DAYS_CREDIT_UPDATE AMT_ANNUITY
count 1.716428e+06 1.716428e+06 1.716428e+06 1.716428e+06 1.610875e+06 1.082775e+06 5.919400e+05 1.716428e+06 1.716415e+06 1.458759e+06 1.124648e+06 1.716428e+06 1.716428e+06 4.896370e+05
mean 2.782149e+05 5.924434e+06 -1.142108e+03 8.181666e-01 5.105174e+02 -1.017437e+03 3.825418e+03 6.410406e-03 3.549946e+05 1.370851e+05 6.229515e+03 3.791276e+01 -5.937483e+02 1.571276e+04
std 1.029386e+05 5.322657e+05 7.951649e+02 3.654443e+01 4.994220e+03 7.140106e+02 2.060316e+05 9.622391e-02 1.149811e+06 6.774011e+05 4.503203e+04 5.937650e+03 7.207473e+02 3.258269e+05
min 1.000010e+05 5.000000e+06 -2.922000e+03 0.000000e+00 -4.206000e+04 -4.202300e+04 0.000000e+00 0.000000e+00 0.000000e+00 -4.705600e+06 -5.864061e+05 0.000000e+00 -4.194700e+04 0.000000e+00
25% 1.888668e+05 5.463954e+06 -1.666000e+03 0.000000e+00 -1.138000e+03 -1.489000e+03 0.000000e+00 0.000000e+00 5.130000e+04 0.000000e+00 0.000000e+00 0.000000e+00 -9.080000e+02 0.000000e+00
50% 2.780550e+05 5.926304e+06 -9.870000e+02 0.000000e+00 -3.300000e+02 -8.970000e+02 0.000000e+00 0.000000e+00 1.255185e+05 0.000000e+00 0.000000e+00 0.000000e+00 -3.950000e+02 0.000000e+00
75% 3.674260e+05 6.385681e+06 -4.740000e+02 0.000000e+00 4.740000e+02 -4.250000e+02 0.000000e+00 0.000000e+00 3.150000e+05 4.015350e+04 0.000000e+00 0.000000e+00 -3.300000e+01 1.350000e+04
max 4.562550e+05 6.843457e+06 0.000000e+00 2.792000e+03 3.119900e+04 0.000000e+00 1.159872e+08 9.000000e+00 5.850000e+08 1.701000e+08 4.705600e+06 3.756681e+06 3.720000e+02 1.184534e+08

8 rows × 14 columns

In [199]:
# Display the above statistical description table, but with axes inverted, so that the table can 
# be included in the project's writeup.
display(bureau_data.describe().transpose())
count mean std min 25% 50% 75% max
SK_ID_CURR 1716428.0 2.782149e+05 1.029386e+05 100001.000 188866.75 278055.0 367426.00 4.562550e+05
SK_ID_BUREAU 1716428.0 5.924434e+06 5.322657e+05 5000000.000 5463953.75 5926303.5 6385681.25 6.843457e+06
DAYS_CREDIT 1716428.0 -1.142108e+03 7.951649e+02 -2922.000 -1666.00 -987.0 -474.00 0.000000e+00
CREDIT_DAY_OVERDUE 1716428.0 8.181666e-01 3.654443e+01 0.000 0.00 0.0 0.00 2.792000e+03
DAYS_CREDIT_ENDDATE 1610875.0 5.105174e+02 4.994220e+03 -42060.000 -1138.00 -330.0 474.00 3.119900e+04
DAYS_ENDDATE_FACT 1082775.0 -1.017437e+03 7.140106e+02 -42023.000 -1489.00 -897.0 -425.00 0.000000e+00
AMT_CREDIT_MAX_OVERDUE 591940.0 3.825418e+03 2.060316e+05 0.000 0.00 0.0 0.00 1.159872e+08
CNT_CREDIT_PROLONG 1716428.0 6.410406e-03 9.622391e-02 0.000 0.00 0.0 0.00 9.000000e+00
AMT_CREDIT_SUM 1716415.0 3.549946e+05 1.149811e+06 0.000 51300.00 125518.5 315000.00 5.850000e+08
AMT_CREDIT_SUM_DEBT 1458759.0 1.370851e+05 6.774011e+05 -4705600.320 0.00 0.0 40153.50 1.701000e+08
AMT_CREDIT_SUM_LIMIT 1124648.0 6.229515e+03 4.503203e+04 -586406.115 0.00 0.0 0.00 4.705600e+06
AMT_CREDIT_SUM_OVERDUE 1716428.0 3.791276e+01 5.937650e+03 0.000 0.00 0.0 0.00 3.756681e+06
DAYS_CREDIT_UPDATE 1716428.0 -5.937483e+02 7.207473e+02 -41947.000 -908.00 -395.0 -33.00 3.720000e+02
AMT_ANNUITY 489637.0 1.571276e+04 3.258269e+05 0.000 0.00 0.0 13500.00 1.184534e+08

14 rows × 8 columns

Bureau Data Table Featureset Definitions

  1. SK_ID_CURR: ID of loan in our sample - one loan in our sample can have 0,1,2 or more related previous credits in credit bureau -- hashed
  2. SK_ID_BUREAU: Recoded ID of previous Credit Bureau credit related to our loan (unique coding for each loan application) -- hashed
  3. CREDIT_ACTIVE: Status of the Credit Bureau (CB) reported credits
  4. CREDIT_CURRENCY: Recoded currency of the Credit Bureau credit -- recoded
  5. DAYS_CREDIT: How many days before current application did client apply for Credit Bureau credit -- time only relative to the application
  6. CREDIT_DAY_OVERDUE: Number of days past due on CB credit at the time of application for related loan in our sample
  7. DAYS_CREDIT_ENDDATE: Remaining duration of CB credit (in days) at the time of application in Home Credit -- time only relative to the application
  8. DAYS_ENDDATE_FACT: Days since CB credit ended at the time of application in Home Credit (only for closed credit) -- time only relative to the application
  9. AMT_CREDIT_MAX_OVERDUE: Maximal amount overdue on the Credit Bureau credit so far (at application date of loan in our sample)
  10. CNT_CREDIT_PROLONG: How many times was the Credit Bureau credit prolonged
  11. AMT_CREDIT_SUM: Current credit amount for the Credit Bureau credit
  12. AMT_CREDIT_SUM_DEBT: Current debt on Credit Bureau credit
  13. AMT_CREDIT_SUM_LIMIT: Current credit limit of credit card reported in Credit Bureau
  14. AMT_CREDIT_SUM_OVERDUE: Current amount overdue on Credit Bureau credit
  15. CREDIT_TYPE: Type of Credit Bureau credit (Car, cash,...)
  16. DAYS_CREDIT_UPDATE: How many days before loan application did last information about the Credit Bureau credit come -- time only relative to the application
  17. AMT_ANNUITY: Annuity of the Credit Bureau credit

3. Bureau Balance Data Table (bureau_balance.csv)

In [24]:
# Display the first five records
display(bureau_balance_data.head(n=5))
SK_ID_BUREAU MONTHS_BALANCE STATUS
0 5715448 0 C
1 5715448 -1 C
2 5715448 -2 C
3 5715448 -3 C
4 5715448 -4 C

Bureau Balance Data Table Featureset Definitions

  1. SK_ID_BUREAU: Recoded ID of Credit Bureau credit (unique coding for each application) - use this to join to CREDIT_BUREAU table -- hashed
  2. MONTHS_BALANCE: Month of balance relative to application date (-1 means the freshest balance date) -- time only relative to the application
  3. STATUS: Status of Credit Bureau loan during the month (active, closed, DPD0-30,Ö [C means closed, X means status unknown, 0 means no DPD, 1 means maximal did during month between 1-30, 2 means DPD 31-60,Ö 5 means DPD 120+ or sold or written off ])

4. Previous Application Data Table (previous_application.csv)

In [26]:
# Display the first five records
display(previous_application_data.head(n=5))
SK_ID_PREV SK_ID_CURR NAME_CONTRACT_TYPE AMT_ANNUITY AMT_APPLICATION AMT_CREDIT AMT_DOWN_PAYMENT AMT_GOODS_PRICE WEEKDAY_APPR_PROCESS_START HOUR_APPR_PROCESS_START ... NAME_SELLER_INDUSTRY CNT_PAYMENT NAME_YIELD_GROUP PRODUCT_COMBINATION DAYS_FIRST_DRAWING DAYS_FIRST_DUE DAYS_LAST_DUE_1ST_VERSION DAYS_LAST_DUE DAYS_TERMINATION NFLAG_INSURED_ON_APPROVAL
0 2030495 271877 Consumer loans 1730.430 17145.0 17145.0 0.0 17145.0 SATURDAY 15 ... Connectivity 12.0 middle POS mobile with interest 365243.0 -42.0 300.0 -42.0 -37.0 0.0
1 2802425 108129 Cash loans 25188.615 607500.0 679671.0 NaN 607500.0 THURSDAY 11 ... XNA 36.0 low_action Cash X-Sell: low 365243.0 -134.0 916.0 365243.0 365243.0 1.0
2 2523466 122040 Cash loans 15060.735 112500.0 136444.5 NaN 112500.0 TUESDAY 11 ... XNA 12.0 high Cash X-Sell: high 365243.0 -271.0 59.0 365243.0 365243.0 1.0
3 2819243 176158 Cash loans 47041.335 450000.0 470790.0 NaN 450000.0 MONDAY 7 ... XNA 12.0 middle Cash X-Sell: middle 365243.0 -482.0 -152.0 -182.0 -177.0 1.0
4 1784265 202054 Cash loans 31924.395 337500.0 404055.0 NaN 337500.0 THURSDAY 9 ... XNA 24.0 high Cash Street: high NaN NaN NaN NaN NaN NaN

5 rows × 37 columns

Previous Application Data Table Featureset Definitions

  1. SK_ID_PREV: ID of previous credit in Home credit related to loan in our sample. (One loan in our sample can have 0,1,2 or more previous loan applications in Home Credit, previous application could, but not necessarily have to lead to credit) -- hashed
  2. SK_ID_CURR: ID of loan in our sample -- hashed
  3. NAME_CONTRACT_TYPE: Contract product type (Cash loan, consumer loan [POS] ,...) of the previous application
  4. AMT_ANNUITY: Annuity of previous application
  5. AMT_APPLICATION: For how much credit did client ask on the previous application
  6. AMT_CREDIT: Final credit amount on the previous application. This differs from AMT_APPLICATION in a way that the AMT_APPLICATION is the amount for which the client initially applied for, but during our approval process he could have received different amount - AMT_CREDIT
  7. AMT_DOWN_PAYMENT: Down payment on the previous application
  8. AMT_GOODS_PRICE: Goods price of good that client asked for (if applicable) on the previous application
  9. WEEKDAY_APPR_PROCESS_START: On which day of the week did the client apply for previous application
  10. HOUR_APPR_PROCESS_START: Approximately at what day hour did the client apply for the previous application -- rounded
  11. FLAG_LAST_APPL_PER_CONTRACT: Flag if it was last application for the previous contract. Sometimes by mistake of client or our clerk there could be more applications for one single contract
  12. NFLAG_LAST_APPL_IN_DAY: Flag if the application was the last application per day of the client. Sometimes clients apply for more applications a day. Rarely it could also be error in our system that one application is in the database twice
  13. RATE_DOWN_PAYMENT: Down payment rate normalized on previous credit -- normalized
  14. RATE_INTEREST_PRIMARY: Interest rate normalized on previous credit -- normalized
  15. RATE_INTEREST_PRIVILEGED: Interest rate normalized on previous credit -- normalized
  16. NAME_CASH_LOAN_PURPOSE: Purpose of the cash loan
  17. NAME_CONTRACT_STATUS: Contract status (approved, cancelled, ...) of previous application
  18. DAYS_DECISION: Relative to current application when was the decision about previous application made time only relative to the application
  19. NAME_PAYMENT_TYPE: Payment method that client chose to pay for the previous application
  20. CODE_REJECT_REASON: Why was the previous application rejected
  21. NAME_TYPE_SUITE: Who accompanied client when applying for the previous application
  22. NAME_CLIENT_TYPE: Was the client old or new client when applying for the previous application
  23. NAME_GOODS_CATEGORY: What kind of goods did the client apply for in the previous application
  24. NAME_PORTFOLIO: Was the previous application for CASH, POS, CAR, Ö
  25. NAME_PRODUCT_TYPE: Was the previous application x-sell o walk-in
  26. CHANNEL_TYPE: Through which channel we acquired the client on the previous application
  27. SELLERPLACE_AREA: Selling area of seller place of the previous application
  28. NAME_SELLER_INDUSTRY: The industry of the seller
  29. CNT_PAYMENT: Term of previous credit at application of the previous application
  30. NAME_YIELD_GROUP: Grouped interest rate into small medium and high of the previous application -- grouped
  31. PRODUCT_COMBINATION: Detailed product combination of the previous application
  32. DAYS_FIRST_DRAWING: Relative to application date of current application when was the first disbursement of the previous application -- time only relative to the application
  33. DAYS_FIRST_DUE: Relative to application date of current application when was the first due supposed to be of the previous application -- time only relative to the application
  34. DAYS_LAST_DUE_1ST_VERSION: Relative to application date of current application when was the first due of the previous application -- time only relative to the application
  35. DAYS_LAST_DUE: Relative to application date of current application when was the last due date of the previous application -- time only relative to the application
  36. DAYS_TERMINATION: Relative to application date of current application when was the expected termination of the previous application -- time only relative to the application
  37. NFLAG_INSURED_ON_APPROVAL: Did the client requested insurance during the previous application

5. POS CASH Balance Data Table (POS_CASH_balance.csv)

In [20]:
# Display the first five records
display(POS_CASH_balance_data.head(n=5))
SK_ID_PREV SK_ID_CURR MONTHS_BALANCE CNT_INSTALMENT CNT_INSTALMENT_FUTURE NAME_CONTRACT_STATUS SK_DPD SK_DPD_DEF
0 1803195 182943 -31 48.0 45.0 Active 0 0
1 1715348 367990 -33 36.0 35.0 Active 0 0
2 1784872 397406 -32 12.0 9.0 Active 0 0
3 1903291 269225 -35 48.0 42.0 Active 0 0
4 2341044 334279 -35 36.0 35.0 Active 0 0

POS CASH Balance Data Table Featureset Definitions

  1. SK_ID_PREV: ID of previous credit in Home Credit related to loan in our sample. (One loan in our sample can have 0,1,2 or more previous loans in Home Credit)
  2. SK_ID_CURR: ID of loan in our sample
  3. MONTHS_BALANCE: Month of balance relative to application date (-1 means the information to the freshest monthly snapshot, 0 means the information at application - often it will be the same as -1 as many banks are not updating the information to Credit Bureau regularly ) -- time only relative to the application
  4. CNT_INSTALMENT: Term of previous credit (can change over time)
  5. CNT_INSTALMENT_FUTURE: Installments left to pay on the previous credit
  6. NAME_CONTRACT_STATUS: Contract status during the month
  7. SK_DPD: DPD (days past due) during the month of previous credit
  8. SK_DPD_DEF: DPD during the month with tolerance (debts with low loan amounts are ignored) of the previous credit

6. Installments Payments Data Table (installments_payments.csv)

In [21]:
# Display the first five records
display(installments_payments_data.head(n=5))
SK_ID_PREV SK_ID_CURR NUM_INSTALMENT_VERSION NUM_INSTALMENT_NUMBER DAYS_INSTALMENT DAYS_ENTRY_PAYMENT AMT_INSTALMENT AMT_PAYMENT
0 1054186 161674 1.0 6 -1180.0 -1187.0 6948.360 6948.360
1 1330831 151639 0.0 34 -2156.0 -2156.0 1716.525 1716.525
2 2085231 193053 2.0 1 -63.0 -63.0 25425.000 25425.000
3 2452527 199697 1.0 3 -2418.0 -2426.0 24350.130 24350.130
4 2714724 167756 1.0 2 -1383.0 -1366.0 2165.040 2160.585

Installments Payments Data Table Featureset Definitions

  1. SK_ID_PREV: ID of previous credit in Home credit related to loan in our sample. (One loan in our sample can have 0,1,2 or more previous loans in Home Credit) -- hashed
  2. SK_ID_CURR: ID of loan in our sample -- hashed
  3. NUM_INSTALMENT_VERSION: Version of installment calendar (0 is for credit card) of previous credit. Change of installment version from month to month signifies that some parameter of payment calendar has changed
  4. NUM_INSTALMENT_NUMBER: On which installment we observe payment
  5. DAYS_INSTALMENT: When the installment of previous credit was supposed to be paid (relative to application date of current loan) -- time only relative to the application
  6. DAYS_ENTRY_PAYMENT: When was the installments of previous credit paid actually (relative to application date of current loan) -- time only relative to the application
  7. AMT_INSTALMENT: What was the prescribed installment amount of previous credit on this installment
  8. AMT_PAYMENT: What the client actually paid on previous credit on this installment

7. Credit Card Balance Data Table (credit_card_balance.csv)

In [22]:
# Display the first five records
display(credit_card_balance_data.head(n=5))
SK_ID_PREV SK_ID_CURR MONTHS_BALANCE AMT_BALANCE AMT_CREDIT_LIMIT_ACTUAL AMT_DRAWINGS_ATM_CURRENT AMT_DRAWINGS_CURRENT AMT_DRAWINGS_OTHER_CURRENT AMT_DRAWINGS_POS_CURRENT AMT_INST_MIN_REGULARITY ... AMT_RECIVABLE AMT_TOTAL_RECEIVABLE CNT_DRAWINGS_ATM_CURRENT CNT_DRAWINGS_CURRENT CNT_DRAWINGS_OTHER_CURRENT CNT_DRAWINGS_POS_CURRENT CNT_INSTALMENT_MATURE_CUM NAME_CONTRACT_STATUS SK_DPD SK_DPD_DEF
0 2562384 378907 -6 56.970 135000 0.0 877.5 0.0 877.5 1700.325 ... 0.000 0.000 0.0 1 0.0 1.0 35.0 Active 0 0
1 2582071 363914 -1 63975.555 45000 2250.0 2250.0 0.0 0.0 2250.000 ... 64875.555 64875.555 1.0 1 0.0 0.0 69.0 Active 0 0
2 1740877 371185 -7 31815.225 450000 0.0 0.0 0.0 0.0 2250.000 ... 31460.085 31460.085 0.0 0 0.0 0.0 30.0 Active 0 0
3 1389973 337855 -4 236572.110 225000 2250.0 2250.0 0.0 0.0 11795.760 ... 233048.970 233048.970 1.0 1 0.0 0.0 10.0 Active 0 0
4 1891521 126868 -1 453919.455 450000 0.0 11547.0 0.0 11547.0 22924.890 ... 453919.455 453919.455 0.0 1 0.0 1.0 101.0 Active 0 0

5 rows × 23 columns

Credit Card Balance Data Table Featureset Definitions

  1. SK_ID_PREV: ID of previous credit in Home credit related to loan in our sample. (One loan in our sample can have 0,1,2 or more previous loans in Home Credit) -- hashed
  2. SK_ID_CURR: ID of loan in our sample -- hashed
  3. MONTHS_BALANCE: Month of balance relative to application date (-1 means the freshest balance date) -- time only relative to the application
  4. AMT_BALANCE: Balance during the month of previous credit
  5. AMT_CREDIT_LIMIT_ACTUAL: Credit card limit during the month of the previous credit
  6. AMT_DRAWINGS_ATM_CURRENT: Amount drawing at ATM during the month of the previous credit
  7. AMT_DRAWINGS_CURRENT: Amount drawing during the month of the previous credit
  8. AMT_DRAWINGS_OTHER_CURRENT: Amount of other drawings during the month of the previous credit
  9. AMT_DRAWINGS_POS_CURRENT: Amount drawing or buying goods during the month of the previous credit
  10. AMT_INST_MIN_REGULARITY: Minimal installment for this month of the previous credit
  11. AMT_PAYMENT_CURRENT: How much did the client pay during the month on the previous credit
  12. AMT_PAYMENT_TOTAL_CURRENT: How much did the client pay during the month in total on the previous credit
  13. AMT_RECEIVABLE_PRINCIPAL: Amount receivable for principal on the previous credit
  14. AMT_RECIVABLE: Amount receivable on the previous credit
  15. AMT_TOTAL_RECEIVABLE: Total amount receivable on the previous credit
  16. CNT_DRAWINGS_ATM_CURRENT: Number of drawings at ATM during this month on the previous credit
  17. CNT_DRAWINGS_CURRENT: Number of drawings during this month on the previous credit
  18. CNT_DRAWINGS_OTHER_CURRENT: Number of other drawings during this month on the previous credit
  19. CNT_DRAWINGS_POS_CURRENT: Number of drawings for goods during this month on the previous credit
  20. CNT_INSTALMENT_MATURE_CUM: Number of paid installments on the previous credit
  21. NAME_CONTRACT_STATUS: Contract status (active signed,...) on the previous credit
  22. SK_DPD: DPD (Days past due) during the month on the previous credit
  23. SK_DPD_DEF: DPD (Days past due) during the month with tolerance (debts with low loan amounts are ignored) of the previous credit

Feature Engineering

What is one feature that could be engineered from the data contained in the six tables that are supplementary to the main data table?

Of the six other tables in the dataset outside of the main data table, four tables (previous_application.csv, POS_CASH_balance.csv, installments_payments.csv, and credit_card_balance.csv) contain information pertaining to previous loan applications, or payback histories on prior loans, that an applicant has had with Home Credit. To engineer a new feature, I instead intend to focus on the two data tables that describe applicants' payback performance with lenders other than Home Credit:

  • bureau.csv
  • bureau_balance.csv

My hypothesis is that out of the six supplementary data tables, the above two tables will be the greatest source of supplementary insight.

bureau.csv contains summary information of applicants' loans from other lenders, such as the amount and type of loan, and the total amount, if any, of the repayment balance that's overdue. bureau_balance.csv contains the month by month statuses (whether a particular month's balance payment was received and processed, or the extent to which payment is overdue) for each loan described in bureau.csv.

Although the month-by-month payment statuses in bureau_balance.csv may prove useful with the right kind of time series analysis, for the purposes of this project I will attempt to engineer a feature that is based on the data contained in the features in bureau.csv. In particular, I will focus on the features in bureau.csv that indicate whether an individual has had difficulty repaying previous loans, the extent of that difficulty, and how recently that difficulty has occurred. Some potentially useful features include:

  • DAYS_CREDIT: Number of days since individual had applied for a loan.
  • DAYS_CREDIT_UPDATE: Number of days since individual's information on the credit bureau was updated.
  • CREDIT_DAY_OVERDUE: Number of days that individual's loan payments have been overdue.
  • AMT_CREDIT_MAX_OVERDUE: Maximum amount individual has ever been overdue on their loan payments.
  • AMT_CREDIT_SUM_OVERDUE: Amount individual is currently overdue on their loan payments.

Since I'm interested in knowing whether a Home Credit loan applicant has had recent difficulty paying back loans they've received from other creditors, I will build a feature based solely on the CREDIT_DAY_OVERDUE feature. For simplicity's sake, my engineered feature will merely indicate whether or not a Home Credit applicant currently has overdue loan payments from other creditors.

My new feature will be titled HAS_CREDIT_BUREAU_LOANS_OVERDUE. If a Home Credit applicant has at least one loan in bureau.csv for which CREDIT_DAY_OVERDUE has a value greater than 0, the value of my new feature in the row belonging to that applicant's Home Credit borrower ID will be 1. Otherwise, the value will be 0. I will engineer this feature during the data preprocessing phase, and once it has been created I will append it to the main data table.

II. Exploratory Visualization

1. Plot Distributions of Numerical Main Data Table Features Identified as Normalized

Home Credit had identified the following 47 numerical features as being normalized. I plotted histograms of each of these features in order to confirm that this is indeed that case, as well as to identify any outliers that might need to be removed.

'REGION_POPULATION_RELATIVE', 'EXT_SOURCE_1', 'EXT_SOURCE_2', 'EXT_SOURCE_3', 'APARTMENTS_AVG', 'BASEMENTAREA_AVG', 'YEARS_BEGINEXPLUATATION_AVG', 'YEARS_BUILD_AVG', 'COMMONAREA_AVG', 'ELEVATORS_AVG', 'ENTRANCES_AVG', 'FLOORSMAX_AVG', 'FLOORSMIN_AVG', 'LANDAREA_AVG', 'LIVINGAPARTMENTS_AVG', 'LIVINGAREA_AVG', 'NONLIVINGAPARTMENTS_AVG', 'NONLIVINGAREA_AVG', 'APARTMENTS_MODE', 'BASEMENTAREA_MODE', 'YEARS_BEGINEXPLUATATION_MODE', 'YEARS_BUILD_MODE', 'COMMONAREA_MODE', 'ELEVATORS_MODE', 'ENTRANCES_MODE', 'FLOORSMAX_MODE', 'FLOORSMIN_MODE', 'LANDAREA_MODE', 'LIVINGAPARTMENTS_MODE', 'LIVINGAREA_MODE', 'NONLIVINGAPARTMENTS_MODE', 'NONLIVINGAREA_MODE', 'APARTMENTS_MEDI', 'BASEMENTAREA_MEDI', 'YEARS_BEGINEXPLUATATION_MEDI', 'YEARS_BUILD_MEDI', 'COMMONAREA_MEDI', 'ELEVATORS_MEDI', 'ENTRANCES_MEDI', 'FLOORSMAX_MEDI', 'FLOORSMIN_MEDI', 'LANDAREA_MEDI', 'LIVINGAPARTMENTS_MEDI', 'LIVINGAREA_MEDI', 'NONLIVINGAPARTMENTS_MEDI', 'NONLIVINGAREA_MEDI', 'TOTALAREA_MODE'

The plotted histograms ignored all 'NaN' entries in each of the above features. Plots for all 47 features can be viewed in the Appendix.

In [34]:
# List of normalized features
normalized_numerical_features = ['REGION_POPULATION_RELATIVE', 'EXT_SOURCE_1', 'EXT_SOURCE_2', 'EXT_SOURCE_3', 'APARTMENTS_AVG', 'BASEMENTAREA_AVG', 'YEARS_BEGINEXPLUATATION_AVG', 'YEARS_BUILD_AVG', 'COMMONAREA_AVG', 'ELEVATORS_AVG', 'ENTRANCES_AVG', 'FLOORSMAX_AVG', 'FLOORSMIN_AVG', 'LANDAREA_AVG', 'LIVINGAPARTMENTS_AVG', 'LIVINGAREA_AVG', 'NONLIVINGAPARTMENTS_AVG', 'NONLIVINGAREA_AVG', 'APARTMENTS_MODE', 'BASEMENTAREA_MODE', 'YEARS_BEGINEXPLUATATION_MODE', 'YEARS_BUILD_MODE', 'COMMONAREA_MODE', 'ELEVATORS_MODE', 'ENTRANCES_MODE', 'FLOORSMAX_MODE', 'FLOORSMIN_MODE', 'LANDAREA_MODE', 'LIVINGAPARTMENTS_MODE', 'LIVINGAREA_MODE', 'NONLIVINGAPARTMENTS_MODE', 'NONLIVINGAREA_MODE', 'APARTMENTS_MEDI', 'BASEMENTAREA_MEDI', 'YEARS_BEGINEXPLUATATION_MEDI', 'YEARS_BUILD_MEDI', 'COMMONAREA_MEDI', 'ELEVATORS_MEDI', 'ENTRANCES_MEDI', 'FLOORSMAX_MEDI', 'FLOORSMIN_MEDI', 'LANDAREA_MEDI', 'LIVINGAPARTMENTS_MEDI', 'LIVINGAREA_MEDI', 'NONLIVINGAPARTMENTS_MEDI', 'NONLIVINGAREA_MEDI', 'TOTALAREA_MODE']

# Plot histogram of each normalized feature, omitting any rows (borrowers) that have a value 
# of 'NaN' for the particular feature
vs.plot_feature_distributions(application_train_data[normalized_numerical_features], title='Distributions of Main Data Table\'s Normalized Features', figsize=(14,60), num_cols=3)

I paid special attention to the distributions of the four features I had found anomalous while initially exploring the dataset, REGION_POPULATION_RELATIVE, EXT_SOURCE_1, EXT_SOURCE_2, and EXT_SOURCE_3.

It turned out that EXT_SOURCE_1, EXT_SOURCE_2, and EXT_SOURCE_3 were the three features I should have been least concerned about -- the shapes of these features' distributions more closely resembled that of the normal bell curve than the shapes of all the other normalized numerical features. And upon closer review, perhaps this shouldn't be so surprising. According to Home Credit's definitions, these three features represent normalized scores that come from an "external data source," and I can only surmise that whatever methodology the external source used to devise and assign these scores may be what causes their values to be more normally distributed across the dataset.

In [4]:
# Plot histogram of ['EXT_SOURCE_1','EXT_SOURCE_2','EXT_SOURCE_3'], omitting any rows (borrowers) that have a value 
# of 'NaN' for the particular feature
vs.plot_feature_distributions(application_train_data[['EXT_SOURCE_1', 'EXT_SOURCE_2', 'EXT_SOURCE_3']], title='Distributions of Features EXT_SOURCE_1, EXT_SOURCE_2, EXT_SOURCE_3', figsize=(14,4), num_cols=3)

As I had discovered while exploring the dataset, the feature REGION_POPULATION_RELATIVE does indeed have all its values within an approximate range of [0.00,0.07]. There is nothing in the feature's definition that suggests why, out of all the other normalized features, that this would be the only feature not scaled to the range [0.0,1.0]. Thankfully, this feature also exhibits minimal positive/negative skewness. The only adjustment necessary would be to min-max scale this feature to the range [0.0,1.0] when preprocessing the data.

In [52]:
# Plot histogram of ['REGION_POPULATION_RELATIVE'], omitting any rows (borrowers) that have a value 
# of 'NaN' for the particular feature
region_population_relative_data = application_train_data['REGION_POPULATION_RELATIVE']
filtered_region_population_relative_data = region_population_relative_data[~np.isnan(region_population_relative_data)]
plt.figure(figsize = (10,6))
plt.hist(filtered_region_population_relative_data, bins=50)
plt.title('Distribution of Feature REGION_POPULATION_RELATIVE')
plt.xlabel('Value')
plt.ylabel('Number of Borrowers')
plt.savefig('distribREGIONPOPULATIONRELATIVE.png')
plt.show()

Other normalized features, particularly those concerning general characteristics of a borrower's residence, such as YEARS_BUILD_AVG, FLOORSMIN_MODE, and FLOORSMAX_AVG also had the appearance of a normal distribution without much positive or negative skewness. This intuitively makes sense as these general features about number of floors and the year the residence was built will pertain to all individuals' residences, regardless of the dwelling type.

In [28]:
# Plot histogram of ['YEARS_BUILD_AVG', 'FLOORSMIN_MODE, 'FLOORSMAX_AVG'], omitting any rows (borrowers) that have a value 
# of 'NaN' for the particular feature
vs.plot_feature_distributions(application_train_data[['YEARS_BUILD_AVG', 'FLOORSMIN_MODE', 'FLOORSMAX_AVG']], title='Distributions of Features YEARS_BUILD_AVG, FLOORSMIN_MODE, FLOORSMAX_AVG', figsize=(14,5), num_cols=3)

On the other hand, the rest of the normalized features, such as ELEVATORS_MEDI, COMMONAREA_MEDI, NONLIVINGAPARTMENTS_MEDI, describe more niche characteristics of a residence building that may not apply to many of the borrowers. For example, individuals who live in a house wouldn't be expected to have any elevators, a public common area, or non-living apartments in their residence. And as such, these features tend to be noticeably positively skewed.

In [33]:
# Plot histogram of ['EXT_SOURCE_1','EXT_SOURCE_2','EXT_SOURCE_3'], omitting any rows (borrowers) that have a value 
# of 'NaN' for the particular feature
vs.plot_feature_distributions(application_train_data[['ELEVATORS_MEDI', 'COMMONAREA_MEDI', 'NONLIVINGAPARTMENTS_MEDI']], title='Distributions of Features ELEVATORS_MEDI, COMMONAREA_MEDI, NONLIVINGAPARTMENTS_MED', figsize=(14,5), num_cols=3)

2. Plot Distributions of Non-Normalized Main Data Table Numerical Features

The following 21 numerical features were not identified by Home Credit as being normalized. I plotted histograms of each of these features in order to observe their skewness and to discover which ones would be candidates for log-normalization:

'AMT_INCOME_TOTAL', 'AMT_CREDIT', 'AMT_ANNUITY', 'AMT_GOODS_PRICE', 'DAYS_BIRTH', 'DAYS_EMPLOYED', 'DAYS_REGISTRATION', 'DAYS_ID_PUBLISH', 'OWN_CAR_AGE', 'HOUR_APPR_PROCESS_START', 'OBS_30_CNT_SOCIAL_CIRCLE', 'DEF_30_CNT_SOCIAL_CIRCLE', 'OBS_60_CNT_SOCIAL_CIRCLE', 'DEF_60_CNT_SOCIAL_CIRCLE', 'DAYS_LAST_PHONE_CHANGE', 'AMT_REQ_CREDIT_BUREAU_HOUR', 'AMT_REQ_CREDIT_BUREAU_DAY', 'AMT_REQ_CREDIT_BUREAU_WEEK', 'AMT_REQ_CREDIT_BUREAU_MON', 'AMT_REQ_CREDIT_BUREAU_QRT', 'AMT_REQ_CREDIT_BUREAU_YEAR'
In [4]:
# List of non-normalized features
non_normalized_numerical_features = ['AMT_INCOME_TOTAL', 'AMT_CREDIT', 'AMT_ANNUITY', 'AMT_GOODS_PRICE', 'DAYS_BIRTH', 'DAYS_EMPLOYED', 'DAYS_REGISTRATION', 'DAYS_ID_PUBLISH', 'OWN_CAR_AGE', 'HOUR_APPR_PROCESS_START', 'OBS_30_CNT_SOCIAL_CIRCLE', 'DEF_30_CNT_SOCIAL_CIRCLE', 'OBS_60_CNT_SOCIAL_CIRCLE', 'DEF_60_CNT_SOCIAL_CIRCLE', 'DAYS_LAST_PHONE_CHANGE', 'AMT_REQ_CREDIT_BUREAU_HOUR', 'AMT_REQ_CREDIT_BUREAU_DAY', 'AMT_REQ_CREDIT_BUREAU_WEEK', 'AMT_REQ_CREDIT_BUREAU_MON', 'AMT_REQ_CREDIT_BUREAU_QRT', 'AMT_REQ_CREDIT_BUREAU_YEAR']

# Plot histogram of each non-normalized feature, omitting any rows (borrowers) that have a value 
# of 'NaN' for the particular feature
vs.plot_feature_distributions(application_train_data[non_normalized_numerical_features], title='Distributions of Main Data Table\'s Non-Normalized Features', figsize=(16,30), num_cols=3)
In [41]:
# Plot histogram of ['DAYS_BIRTH','DAYS_ID_PUBLISH','HOUR_APPR_PROCESS_START'], omitting any rows (borrowers) that have a value 
# of 'NaN' for the particular feature
vs.plot_feature_distributions(application_train_data[['DAYS_BIRTH','DAYS_ID_PUBLISH','HOUR_APPR_PROCESS_START']], title='Distributions of Features DAYS_BIRTH, DAYS_ID_PUBLISH, HOUR_APPR_PROCESS_START', figsize=(14,5), num_cols=3)

While all of these 21 features will need to be min-max scaled to a range of [0.0,1.0], the following three features already exhibit non-skewed, normal shaped distributions. It will not be necessary for them to be log-normalized:

  • DAYS_BIRTH
  • DAYS_ID_PUBLISH
  • HOUR_APPR_PROCESS_START

The rest of the features, especially those with majority of their values concentrated close to zero yet also having a smattering of large-valued data points, are good candidates for log-normalization. Doing this may prevent these features' very large and very small values from negatively affecting the performance of a learning algorithm.

There are three non-normalized features DAYS_EMPLOYED, DAYS_REGISTRATION, and DAYS_LAST_PHONE_CHANGE that both have skewed distributions, as well as values that fall inside a range of negative numbers. Because log-transformation cannot be run on negative values, these features' distributions would first need to be translated positively to the right, such that all their values are greater than or equal to zero.

In [35]:
# Plot histogram of ['DAYS_EMPLOYED','DAYS_REGISTRATION','DAYS_LAST_PHONE_CHANGE'], omitting any rows (borrowers) that have skewed 
# distributions over a range of negative values.
vs.plot_feature_distributions(application_train_data[['DAYS_EMPLOYED','DAYS_REGISTRATION','DAYS_LAST_PHONE_CHANGE']], title='Distributions of Features DAYS_EMPLOYED, DAYS_REGISTRATION, DAYS_LAST_PHONE_CHANGE', figsize=(14,5), num_cols=3)

Unfortunately, DAYS_EMPLOYED and OWN_CAR_AGE contain an alarming amount of high-valued outliers that likely need to be addressed. I will further explore each of these features below.

3. Deeper Dive into the DAYS_EMPLOYED Feature

I paid extra close attention to the histogram of DAYS_EMPLOYED, in order to observe whether it has other impossibly large, positive entries similar to the value of 365,243 days, or just over 1,000 years, that I observed above.

In [54]:
# Draw a larger plot of the DAYS_EMPLOYED feature's histogram
plt.figure(figsize = (10,6))
plt.hist(application_train_data['DAYS_EMPLOYED'], bins=50)
plt.title('DAYS_EMPLOYED Distribution')
plt.xlabel('Value')
plt.ylabel('Number of Borrowers')
plt.savefig('distribDAYSEMPLOYED.png')
plt.show()
In [66]:
application_train_data['DAYS_EMPLOYED'].value_counts().sort_index(ascending=False)
Out[66]:
 365243    55374
 0             2
-1             1
-2             2
-3             3
-4             4
-5             1
-6             2
-7             1
-8             2
-9             3
-10            1
-11            2
-12            6
-13            3
-14            3
-15            6
-16            7
-17            5
-18            3
-19            3
-20            3
-21            2
-22            2
-23            1
-24            3
-25            3
-26            7
-27            2
-28            4
-29            5
-30            4
-31            8
-32            2
-33            2
-34            7
-35            3
-36            3
-37            5
-38           10
-39            2
-40            3
-41           12
-42           10
-43           13
-44            7
-45            6
-46           10
-47            4
-48            6
-49            9
-50            3
-51           10
-52            6
-53            6
-54           11
-55           10
-56           11
-57           11
-58            9
-59           12
-60            6
-61            6
-62           11
-63           11
-64           11
-65           12
-66           14
-67           15
-68           16
-69           15
-70            9
-71            8
-72           11
-73           16
-74           22
-75           15
-76           23
-77           18
-78           24
-79           24
-80           17
-81           35
-82           28
-83           17
-84           22
-85           30
-86           28
-87           27
-88           29
-89           35
-90           41
-91           37
-92           62
-93           52
-94           51
-95           62
-96           81
-97           69
-98           81
-99           78
-100          63
-101          81
-102         103
-103          75
-104          95
-105         105
-106         114
-107         104
-108          99
-109         115
-110          81
-111          96
-112         111
-113         111
-114          77
-115         117
-116         136
-117         102
-118         119
-119         116
-120          99
-121          98
-122          99
-123         104
-124         107
-125         112
-126         106
-127         111
-128          98
-129         116
-130         109
-131          86
-132         117
-133         117
-134         100
-135         112
-136         105
-137         123
-138          98
-139         122
-140         105
-141         109
-142          94
-143         108
-144          94
-145          77
-146         108
-147         100
-148          90
-149          82
-150          90
-151          70
-152          81
-153          96
-154          87
-155          91
-156          90
-157         104
-158          83
-159          87
-160          85
-161          99
-162         100
-163         114
-164         109
-165          99
-166          76
-167         105
-168          92
-169          94
-170         106
-171          93
-172          95
-173          95
-174          96
-175          82
-176          91
-177         108
-178          98
-179         108
-180         100
-181         118
-182         108
-183         101
-184         126
-185         128
-186         110
-187         106
-188         137
-189         102
-190          97
-191         120
-192         114
-193         134
-194         127
-195         133
-196         136
-197         114
-198         122
-199         151
-200         156
-201         130
-202         110
-203         112
-204         116
-205         120
-206         115
-207         138
-208         115
-209         125
-210         112
-211         104
-212         150
-213         131
-214         132
-215         138
-216         137
-217         111
-218         120
-219         106
-220         110
-221         106
-222         135
-223         119
-224         152
-225         119
-226         107
-227         118
-228         117
-229         143
-230         151
-231         140
-232         105
-233         128
-234         126
-235         100
-236         115
-237         127
-238         122
-239          93
-240         120
-241         124
-242         103
-243          90
-244         118
-245          79
-246         107
-247          93
-248         106
           ...  
-14441         2
-14445         1
-14452         1
-14453         1
-14455         1
-14456         2
-14460         1
-14468         2
-14473         3
-14474         1
-14478         1
-14482         1
-14487         1
-14507         2
-14509         1
-14513         1
-14522         3
-14536         1
-14540         2
-14541         1
-14543         1
-14553         1
-14556         1
-14559         2
-14571         1
-14574         1
-14584         1
-14589         1
-14590         3
-14597         1
-14604         1
-14607         1
-14619         1
-14620         1
-14627         1
-14628         1
-14637         1
-14640         1
-14643         1
-14644         1
-14648         1
-14651         1
-14660         1
-14666         1
-14679         1
-14688         1
-14689         1
-14691         1
-14694         1
-14701         1
-14719         1
-14722         2
-14723         1
-14726         1
-14743         1
-14747         1
-14749         1
-14756         1
-14775         1
-14778         2
-14786         1
-14797         1
-14801         1
-14802         1
-14805         1
-14810         1
-14819         1
-14829         1
-14832         1
-14833         1
-14848         1
-14849         1
-14850         1
-14854         1
-14860         2
-14872         1
-14887         1
-14894         1
-14910         2
-14915         1
-14928         1
-14933         1
-14949         1
-14955         1
-14957         1
-14966         1
-14968         1
-14974         1
-14977         1
-14978         1
-14981         1
-14985         2
-14988         1
-14990         1
-14991         1
-15019         1
-15021         1
-15027         1
-15034         1
-15038         1
-15043         1
-15048         1
-15051         2
-15052         1
-15057         1
-15059         1
-15060         1
-15066         2
-15072         1
-15080         1
-15084         1
-15087         1
-15094         1
-15095         1
-15107         2
-15116         1
-15128         1
-15137         1
-15147         2
-15153         1
-15154         1
-15155         1
-15180         1
-15181         1
-15183         1
-15202         1
-15203         1
-15210         1
-15226         1
-15227         1
-15229         1
-15238         1
-15277         1
-15285         1
-15290         1
-15300         1
-15303         1
-15323         1
-15327         1
-15334         1
-15338         1
-15342         1
-15348         1
-15360         1
-15368         1
-15369         1
-15371         1
-15382         1
-15396         1
-15412         1
-15422         1
-15427         1
-15431         1
-15439         1
-15473         1
-15474         2
-15476         1
-15477         1
-15488         1
-15499         1
-15509         2
-15516         1
-15524         1
-15530         2
-15542         1
-15543         1
-15568         1
-15569         1
-15578         1
-15625         1
-15629         1
-15632         1
-15661         1
-15676         1
-15687         1
-15688         1
-15689         1
-15691         1
-15699         1
-15713         1
-15726         1
-15727         1
-15729         1
-15783         1
-15791         1
-15834         1
-15837         1
-15845         2
-15860         1
-15871         1
-15882         1
-15890         1
-15911         1
-15943         1
-16032         1
-16037         1
-16061         1
-16069         1
-16093         1
-16103         1
-16113         1
-16121         1
-16133         1
-16135         1
-16142         1
-16160         1
-16169         1
-16220         1
-16221         1
-16236         1
-16260         1
-16263         1
-16265         1
-16266         1
-16304         1
-16308         1
-16310         1
-16314         1
-16343         1
-16348         1
-16352         1
-16358         1
-16360         1
-16364         1
-16365         1
-16375         1
-16424         1
-16429         1
-16452         1
-16481         1
-16492         1
-16495         2
-16499         1
-16538         1
-16554         1
-16607         1
-16632         1
-16651         1
-16678         1
-16767         1
-16836         1
-16849         1
-16852         1
-17139         1
-17170         1
-17522         1
-17531         1
-17546         1
-17583         1
-17912         1
Name: DAYS_EMPLOYED, Length: 12574, dtype: int64

Based on the definition of DAYS_EMPLOYED, valid values should be in the range (-inf,0]. Unfortunately, 55,374 entries, or nearly one-sixth of the entire training dataset, has an entry of 365243 for this feature. Thankfully, DAYS_EMPLOYED has no 'NaN' entries. Were this value to be interpreted literally, it would indicate that one-sixth of the dataset got a job just over 1,000 years after submitting their loan applications to Home Credit.

This meaning is obviously absurd and there has to be another reason that so many borrowers had the value of 365243 entered for this feature. Since there were no instances of borrowers having a different non-zero positive value for this feature, my best guess is that 365243 was not meant to indicate a numerical value. I believe that this value was entered for applicants who did not have a job when they submitted their loan application to Home Credit. Since any negative integer or 0 would be a valid entry for this feature, and perhaps due to a data entry system's inability to accept any value besides an integer, I hypothesize that the original data enterers simply entered the largest positive integer that the system would accept in order to indicate that the applicant didn't have a job.

Intuitively, I can see that DAYS_EMPLOYED may well be a good predictor of target segments -- after all, if someone doesn't have a job, it stands to reason that there is a greater chance they won't have enough money to make loan payments on time. Unfortunately, I am not confident that the feature, as currently structured, would be able to adequately convey this information to a learning algorithm.

I propose to replace the DAYS_EMPLOYED feature with a new categorical feature called HAS_JOB. All individuals who have a value of 365243 for DAYS_EMPLOYED will be assigned a value of 0 for HAS_JOB. All individuals who have a value of 0 or less for DAYS_EMPLOYED will be assigned a value of 1 for HAS_JOB.

4. Deeper Dive into the OWN_CAR_AGE Feature

In [56]:
# Plot histogram of ['OWN_CAR_AGE'], omitting any rows (borrowers) that have a value 
# of 'NaN' for the particular feature
own_car_age_data = application_train_data['OWN_CAR_AGE']
filtered_own_car_age_data = own_car_age_data[~np.isnan(own_car_age_data)]
plt.figure(figsize = (10,6))
plt.hist(filtered_own_car_age_data, bins=50)
plt.title('Distribution of Feature OWN_CAR_AGE')
plt.xlabel('Value')
plt.ylabel('Number of Borrowers')
plt.savefig('distribOWNCARAGE.png')
plt.show()

There appear to be just under 4,000 borrowers who have a value for OWN_CAR_AGE that's between 60 and 70. This is far too many people to indicate anything but some sort of anomaly. The only kinds of cars that are still functional after 60+ years are collectible classic cars, and folks who apply for loans from Home Credit come from a far less well-off demographic than that which is associated with classic car collection.

In [40]:
application_train_data['OWN_CAR_AGE'].value_counts().sort_index(ascending=False)
Out[40]:
91.0       2
69.0       1
65.0     891
64.0    2443
63.0       2
57.0       1
56.0       1
55.0       4
54.0      12
52.0       1
51.0       3
50.0       1
49.0       6
48.0       1
47.0       1
46.0       5
45.0      11
44.0      21
43.0      19
42.0      42
41.0      58
40.0      85
39.0      78
38.0      97
37.0      75
36.0     124
35.0     157
34.0     183
33.0     132
32.0     208
31.0     267
30.0     326
29.0     397
28.0     542
27.0     483
26.0     580
25.0     865
24.0    1150
23.0    1067
22.0    1250
21.0    1462
20.0    1527
19.0    1864
18.0    2418
17.0    2899
16.0    3355
15.0    3580
14.0    4594
13.0    4566
12.0    4257
11.0    4161
10.0    4806
9.0     5020
8.0     5887
7.0     7424
6.0     6382
5.0     3595
4.0     5557
3.0     6370
2.0     5852
1.0     5280
0.0     2134
Name: OWN_CAR_AGE, Length: 62, dtype: int64

Nonetheless, based on the unique value counts above, I can see right away that the distribution of OWN_CAR_AGE is less problematic than was the case for DAYS_EMPLOYED. There is at least a smooth decrease in numbers of users having older cars, right up until the anomalous spike.

However, the distribution's pattern would indicate that I should expect only one or two borrowers each to have cars that are 64 and 65 years old. I can't hazard a reasonable guess that could explain why a total of 3,334 individuals have cars aged 64 or 65 years, and I can't formulate a compelling justification for removing these entries from the OWN_CAR_AGE feature. The good news is that this is probably ok. Since 3,334 borrowers is only just over 3% of the OWN_CAR_AGE feature's 104,582 valid non-'NaN' entries, log-normalizing this feature should be enough to counteract any negative effects that the anomalous spike may have on a learning algorithm.

III. Algorithms and Techniques

In this section I lay out a roadmap for devising a learning algorithm that predicts which borrowers will make at least one late loan payment (which borrowers have a TARGET value of 1). My approach takes into account everything I learned while exploring and visualizing the main data table.

Data Preprocessing:

  1. Create 7 lists that categorize the features into following different groups based on how each feature will need to be preprocessed: categorical features needing one-hot encoding, binary categorical features, non-normalized numerical features with skewed distributions and negative values, non-normalized numerical features with skewed distributions and only positive values, numerical features wit normal distributions but not scaled to the range [0,1], numerical features with normal distributions and scaled to the range [0,1], and finally, features that will be re-engineered and transformed into different features.
  2. Separate targets column from training dataset.
  3. Use train_test_split from sklearn.cross_validation to create a test validation set that is 20% of the size of the total training set: Will allow me to compare performance of various learning algorithms without overfitting to the training data.
  4. Use the numerical CNT_CHILDREN feature to engineer a binary categorical feature called HAS_CHILDREN.
  5. Drop CNT_CHILDREN from the main dataframe.
  6. Use the CNT_FAM_MEMBERS feature to engineer a categorical feature called FAMILY_SIZE.
  7. Drop CNT_FAM_MEMBERS from the main dataframe.
  8. Use the CREDIT_DAY_OVERDUE feature in bureau.csv to engineer the binary categorical HAS_CREDIT_BUREAU_LOANS_OVERDUE feature.
  9. Use the DAYS_EMPLOYED feature to engineer a binary categorical feature called HAS_JOB.
  10. Drop the DAYS_EMPLOYED feature from the main dataframe.
  11. Translate to positive ranges all values of the 2 non-normalized numerical features that have skewed distributions and negative values: DAYS_REGISTRATION, and DAYS_LAST_PHONE_CHANGE.
  12. Log-transform all 17 non-normalized numerical features that have skewed distributions. These 17 features include the 2 that were translated to positive ranges in Step 11.
  13. Replace 'NaN' values for all numerical features with each feature's mean using Sklearn's Imputer class.
  14. Remove the borrower ID column, SK_ID_CURR, from the main dataframe.
  15. One-hot encode all 19 non-binary categorical features.
  16. Replace all 'NaN' values in all binary categorical features with 0.
  17. Fit a min-max scaler to each of the 17 log-transformed numerical features, as well as to the features DAYS_BIRTH, DAYS_ID_PUBLISH, HOUR_APPR_PROCESS_START, and the normalized feature REGION_POPULATION_RELATIVE. Each feature will be scaled to a range [0.0, 1.0].
  18. Build a data preprocessing pipeline to used for all testing sets. This pipeline will recreate all features that were engineered in the training set during the original data preprocessing phase. It will impute missing numerical values with each column's mean, and replace missing 'NaN' values of binary categorical features with 0. The pipeline will also apply the min-max scaling transforms originally fit on features in the training set to all data points in a testing set. Finally, any columns representing binary one-hot encoded features that are in the training set but absent from a test set will be added to the test set and filled with all 0s. Similarly, any columns in a test dataframe that represent one-hot encoded features that aren't present in the training set will be removed from the test dataframe.
  19. Run this pipeline to preprocess the test validation set.

Implementation:

  1. Create an ROC area-under-curve scorer method.
  2. Use the Gaussian Naive Bayes classifier to make predictions on the test validation set. Calculate the area under ROC curve score of these predictions.
  3. Perform GridSearchCV on an AdaBoost classifier learning algorithm to discover the highest scoring hyperparameter combination by exploring the trade-off between the learning_rate and n_estimators parameters.
  4. Make predictions on the test validation set using both the AdaBoost classifier with the hyperparameters that had been chosen using GridSearchCV. Calculate the area under ROC curve score of these predictions.
  5. Use a Logistic Regression classifier to make predictions, and calculate its ROC AUC score.
  6. Use a Multi Layer Perceptron classifier to make predictions, and calculate its ROC AUC score.
  7. Use a LightGBM classifier to make predictions, and calculate its ROC AUC score.

Refinement:

  1. Use PCA to reduce the dimensionality of the 67 numerical features that are being used.
    1. Fit PCA on all 67 numerical features, trying different values for the n_components parameter, observing how many principle components are needed to explain roughly 90% of the variance in the numerical features.
    2. Use PCA to reduce the dimension space of the numerical features to the optimal number of principle components discovered in Step 1. Create a dataframe to contain these reduced features.
    3. Drop all 67 numerical features from the original preprocessed dataframe, so that it only contains the 184 binary categorical features. Append the dataframe containing the reduced numerical features back to this original dataframe.
  2. Use SelectKBest with different values of k to see if there is a group of k features that causes the learning algorithms to perform better when they are trained on just these k features.
  3. Select the featurespace (All, PCA-reduced, or SelectKBest) and learning algorithm that has had the highest ROC AUC score on the test validation set thus far. Further tune this algorithm's hyperparameters, using GridSearchCV to gain further intuition while tuning.
  4. Create a prediction pipeline that uses the best performing learning algorithm to make predictions on a the actual test datatable, application_test.csv, returns the area under the ROC curve as a score, and outputs a CSV file containing the posterior probabilities of the classifier's predictions for a testing data set.
  5. Train the learning algorithm on the entire training data table in application_train.csv.
  6. Preprocess the data in application_test.csv.
  7. Make predictions on the test data set in application_test.csv using the best learning algorithm. Submit the CSV file containing prediction probabilities to Kaggle and observe the score received from Kaggle.

IV. Data Preprocessing

In [578]:
# Some imports are redundant with imports made in the early code blocks 
# of this notebook. Repeated here for convenience, so that code blocks 
# from much higher up don't have to be re-executed when re-initiating 
# this notebook.

# Import necessary libraries.
import numpy as np
import pandas as pd
import matplotlib as mpl
import matplotlib.pyplot as plt
import seaborn as sns

# Import supplementary visualizations code visuals.py
import visuals as vs

# Display matplotlib plots inline in this notebook.
%matplotlib inline
# Make plots display well on retina displays
%config InlineBackend.figure_format = 'retina'
# Set dpi of plots displayed inline
mpl.rcParams['figure.dpi'] = 300
# Configure style of plots
plt.style.use('fivethirtyeight')
# Make plots smaller
sns.set_context('paper')  

# Allows the use of display() for dataframes.
from IPython.display import display
# Have all columns appear when dataframes are displayed.
pd.set_option('display.max_columns', None) 
# Have 100 rows appear when a dataframe is displayed
pd.set_option('display.max_rows', 500)
# Display dimensions whenever a dataframe is printed out.
pd.set_option('display.show_dimensions', True)

# Import data preprocessing libraries
from sklearn.preprocessing import Imputer
from sklearn.preprocessing import MinMaxScaler

# Import feature selection/dimensionality reduction libraries
from sklearn.decomposition import PCA
from sklearn.feature_selection import SelectKBest, f_classif, chi2

# Import learning algorithms
from sklearn.naive_bayes import GaussianNB
from sklearn.ensemble import AdaBoostClassifier

# Import ROC area-under-curve score
from sklearn.metrics import roc_auc_score
from sklearn.metrics import make_scorer

# Import train-test split, ShuffleSplit, GridSearchCV, and K-fold cross validation
from sklearn.model_selection import train_test_split
from sklearn.model_selection import PredefinedSplit
from sklearn.model_selection import ShuffleSplit
from sklearn.model_selection import GridSearchCV, ParameterGrid
from sklearn.model_selection import StratifiedKFold

# Import a Logistic Regression classifier
from sklearn.linear_model import LogisticRegression

# Import a Multi-layer Perceptron classifier
from sklearn.neural_network import MLPClassifier

# Import a LightGBM classifier
import lightgbm as lgb

# In order to create CSV files
import csv
In [579]:
# Load the main data tables
application_train_data = pd.read_csv("data/application_train.csv")
application_test_data = pd.read_csv("data/application_test.csv")

# Load the Bureau data table
bureau_data = pd.read_csv("data/bureau.csv")
In [580]:
# Step 1: Create lists of different feature types in the main data 
# frame, based on how each type will need to be preprocessed.

# 1. All 18 categorical features needing one-hot encoding. 
#    Includes the 4 categorical features originally 
#    mis-identified as having been normalized:
#    EMERGENCYSTATE_MODE, HOUSETYPE_MODE, WALLSMATERIAL_MODE, 
#    FONDKAPREMONT_MODE
cat_feat_need_one_hot = [
    'NAME_CONTRACT_TYPE', 'CODE_GENDER', 'FLAG_OWN_CAR', 
    'FLAG_OWN_REALTY', 'NAME_INCOME_TYPE', 'NAME_EDUCATION_TYPE', 
    'NAME_FAMILY_STATUS', 'NAME_HOUSING_TYPE', 'REGION_RATING_CLIENT',
    'REGION_RATING_CLIENT_W_CITY', 'WEEKDAY_APPR_PROCESS_START', 'ORGANIZATION_TYPE',
    'NAME_TYPE_SUITE', 'OCCUPATION_TYPE', 'EMERGENCYSTATE_MODE',
    'HOUSETYPE_MODE', 'WALLSMATERIAL_MODE', 'FONDKAPREMONT_MODE'
]

# 2. All 32 binary categorical features already one-hot encoded.
bin_cat_feat = [
    'FLAG_MOBIL', 'FLAG_EMP_PHONE', 'FLAG_WORK_PHONE',
    'FLAG_CONT_MOBILE', 'FLAG_PHONE', 'FLAG_EMAIL',
    'REG_REGION_NOT_LIVE_REGION', 'REG_REGION_NOT_WORK_REGION', 'LIVE_REGION_NOT_WORK_REGION',
    'REG_CITY_NOT_LIVE_CITY', 'REG_CITY_NOT_WORK_CITY', 'LIVE_CITY_NOT_WORK_CITY',
    'FLAG_DOCUMENT_2', 'FLAG_DOCUMENT_3', 'FLAG_DOCUMENT_4',
    'FLAG_DOCUMENT_5', 'FLAG_DOCUMENT_6', 'FLAG_DOCUMENT_7',
    'FLAG_DOCUMENT_8', 'FLAG_DOCUMENT_9', 'FLAG_DOCUMENT_10',
    'FLAG_DOCUMENT_11', 'FLAG_DOCUMENT_12', 'FLAG_DOCUMENT_13',
    'FLAG_DOCUMENT_14', 'FLAG_DOCUMENT_15', 'FLAG_DOCUMENT_16',
    'FLAG_DOCUMENT_17', 'FLAG_DOCUMENT_18', 'FLAG_DOCUMENT_19',
    'FLAG_DOCUMENT_20', 'FLAG_DOCUMENT_21'
]

# 3. All 2 non-normalized numerical features with skewed distributions 
#    and negative values. These features will need to have their 
#    distributions translated to positive ranges before being 
#    log-transformed, and then later scaled to the range [0,1].
non_norm_feat_neg_values_skewed = [
    'DAYS_REGISTRATION', 'DAYS_LAST_PHONE_CHANGE'
]


# 4. All 15 non-normalized numerical features with skewed distributions, 
#    and only positive values. These features will need to be 
#    log-transformed, and eventually scaled to the range [0,1].
non_norm_feat_pos_values_skewed = [
    'AMT_INCOME_TOTAL', 'AMT_CREDIT', 'AMT_ANNUITY',
    'AMT_GOODS_PRICE', 'OBS_30_CNT_SOCIAL_CIRCLE', 'DEF_30_CNT_SOCIAL_CIRCLE',
    'OBS_60_CNT_SOCIAL_CIRCLE', 'DEF_60_CNT_SOCIAL_CIRCLE', 'AMT_REQ_CREDIT_BUREAU_HOUR',
    'AMT_REQ_CREDIT_BUREAU_DAY', 'AMT_REQ_CREDIT_BUREAU_WEEK', 'AMT_REQ_CREDIT_BUREAU_MON',
    'AMT_REQ_CREDIT_BUREAU_QRT', 'AMT_REQ_CREDIT_BUREAU_YEAR', 'OWN_CAR_AGE'
]

# 5. All 4 numerical features with normal shapes but needing to be scaled 
#    to the range [0,1].
norm_feat_need_scaling = [
    'DAYS_BIRTH', 'DAYS_ID_PUBLISH', 'HOUR_APPR_PROCESS_START',
    'REGION_POPULATION_RELATIVE'
]

# 6. All 46 numerical features that have been normalized to the range 
#   [0,1]. These features will need neither log-transformation, nor 
#   any further scaling.
norm_feat_not_need_scaling = [
    'EXT_SOURCE_2', 'EXT_SOURCE_3', 'YEARS_BEGINEXPLUATATION_AVG',
    'YEARS_BEGINEXPLUATATION_MODE', 'YEARS_BEGINEXPLUATATION_MEDI', 'FLOORSMAX_AVG', 
    'FLOORSMAX_MODE', 'FLOORSMAX_MEDI', 'LIVINGAREA_AVG',
    'LIVINGAREA_MODE', 'LIVINGAREA_MEDI', 'ENTRANCES_AVG',
    'ENTRANCES_MODE', 'ENTRANCES_MEDI', 'APARTMENTS_AVG',
    'APARTMENTS_MODE', 'APARTMENTS_MEDI', 'ELEVATORS_AVG', 
    'ELEVATORS_MODE', 'ELEVATORS_MEDI', 'NONLIVINGAREA_AVG',
    'NONLIVINGAREA_MODE', 'NONLIVINGAREA_MEDI', 'EXT_SOURCE_1',
    'BASEMENTAREA_AVG', 'BASEMENTAREA_MODE', 'BASEMENTAREA_MEDI',
    'LANDAREA_AVG', 'LANDAREA_MODE', 'LANDAREA_MEDI',
    'YEARS_BUILD_AVG', 'YEARS_BUILD_MODE', 'YEARS_BUILD_MEDI',
    'FLOORSMIN_AVG', 'FLOORSMIN_MODE', 'FLOORSMIN_MEDI',
    'LIVINGAPARTMENTS_AVG', 'LIVINGAPARTMENTS_MODE', 'LIVINGAPARTMENTS_MEDI',
    'NONLIVINGAPARTMENTS_AVG', 'NONLIVINGAPARTMENTS_MODE', 'NONLIVINGAPARTMENTS_MEDI',
    'COMMONAREA_AVG', 'COMMONAREA_MODE', 'COMMONAREA_MEDI',
    'TOTALAREA_MODE'
]

# 7. The remaining 3 features in the main data frame that will be 
#    re-engineered and transformed into different features
feat_to_be_reengineered = [
    'CNT_CHILDREN', 'CNT_FAM_MEMBERS', 'DAYS_EMPLOYED'
]
In [581]:
# Verify that all 120 features in the main data frame have been categorized 
# according to how they will be preprocessed.
count_of_categorized_features = len(cat_feat_need_one_hot) + len(bin_cat_feat) + len(non_norm_feat_neg_values_skewed)\
+ len(non_norm_feat_pos_values_skewed) + len(norm_feat_need_scaling) + len(norm_feat_not_need_scaling) + len(feat_to_be_reengineered)
print('Number of features in main data frame that have been categorized: {}. Expected: 120.'.format(count_of_categorized_features))
Number of features in main data frame that have been categorized: 120. Expected: 120.
In [582]:
#Step 2: Separate target data from training dataset.
targets = application_train_data['TARGET']
features_raw = application_train_data.drop('TARGET', axis = 1)
In [583]:
# Step 3: Use train_test_split from sklearn.cross_validation to 
# create a test validation set that is 20% of the size of the total training set: 
# Will allow me to compare performance of various learning algorithms without 
# overfitting to the training data.
X_train_raw, X_test_raw, y_train, y_test = train_test_split(features_raw, 
                                                    targets, 
                                                    test_size = 0.2, 
                                                    random_state = 42)
In [584]:
# Step 4: Use the CNT_CHILDREN feature to engineer a binary 
# categorical feature called HAS_CHILDREN. If value of CNT_CHILDREN is 
# greater than 0, the value of HAS_CHILDREN will be 1. If value of CNT_CHILDREN is 
# 0, value of HAS_CHILDREN will be 0. 
CNT_CHILDREN_train = X_train_raw['CNT_CHILDREN']
HAS_CHILDREN = CNT_CHILDREN_train.map(lambda x: 1 if x > 0 else 0)

# Append the newly engineered HAS_CHILDREN feature to the main dataframe.
X_train_raw = X_train_raw.assign(HAS_CHILDREN=HAS_CHILDREN.values)
In [585]:
# Step 5: Drop the CNT_CHILDREN column from the main dataframe
X_train_raw = X_train_raw.drop('CNT_CHILDREN', axis=1)

# Add the new HAS_CHILDREN feature to the list of binary categorical 
# features that are already one-hot encoded. There are now 33 such features.
bin_cat_feat = bin_cat_feat + ['HAS_CHILDREN']
In [586]:
# Step 6. Use the CNT_FAM_MEMBERS feature to engineer a categorical feature called NUMBER_FAMILY_MEMBERS. 
# If CNT_FAM_MEMBERS is 1.0, then the value of NUMBER_FAMILY_MEMBERS will be 'one'. If CNT_FAM_MEMBERS is 2.0, 
# then NUMBER_FAMILY_MEMBERS will be 'two'. If CNT_FAM_MEMBERS is 3.0 or greater, then NUMBER_FAMILY_MEMBERS will 
# be 'three_plus'.
CNT_FAM_MEMBERS_train = X_train_raw['CNT_FAM_MEMBERS']
NUMBER_FAMILY_MEMBERS = CNT_FAM_MEMBERS_train.map(lambda x: 'one' if x == 1 else ('two' if x == 2 else 'three_plus'))

# Append the newly engineered FAMILY_SIZE feature to the main dataframe.
X_train_raw = X_train_raw.assign(NUMBER_FAMILY_MEMBERS=NUMBER_FAMILY_MEMBERS.values)
In [587]:
# Step 7. Drop the CNT_FAM_MEMBERS feature from the main dataframe
X_train_raw = X_train_raw.drop('CNT_FAM_MEMBERS', axis=1)

# Add the new NUMBER_FAMILY_MEMBERS feature to the list of categorical 
# features that will need to be one-hot encoded. There are now 19 of these features.
cat_feat_need_one_hot = cat_feat_need_one_hot + ['NUMBER_FAMILY_MEMBERS']
In [588]:
# Step 8. Use the CREDIT_DAY_OVERDUE feature in bureau.csv to engineer the binary 
# categorical HAS_CREDIT_BUREAU_LOANS_OVERDUE feature. If CREDIT_DAY_OVERDUE for a 
# particular borrower ID (SK_ID_CURR) is greater than 0, then the value of 
# HAS_CREDIT_BUREAU_LOANS_OVERDUE will be 1. If CREDIT_DAY_OVERDUE for a particular 
# borrower ID is 0, then the value of HAS_CREDIT_BUREAU_LOANS_OVERDUE will be 0.

# Filter the bureau data table for loans which are overdue (have a value 
# for CREDIT_DAY_OVERDUE that's greater than 0)
bureau_data_filtered_for_overdue = bureau_data[bureau_data['CREDIT_DAY_OVERDUE'] > 0]

def build_feature_HAS_CREDIT_BUREAU_LOANS_OVERDUE(dataframe):
    """
    Use the CREDIT_DAY_OVERDUE feature in bureau.csv to engineer the binary 
    categorical HAS_CREDIT_BUREAU_LOANS_OVERDUE feature. If CREDIT_DAY_OVERDUE for a 
    particular borrower ID (SK_ID_CURR) is greater than 0, then the value of 
    HAS_CREDIT_BUREAU_LOANS_OVERDUE will be 1. If CREDIT_DAY_OVERDUE for a particular 
    borrower ID is 0, then the value of HAS_CREDIT_BUREAU_LOANS_OVERDUE will be 0.

    Parameters:
        dataframe: Pandas dataframe containing a training or testing dataset
        
    Returns: The dataframe with HAS_CREDIT_BUREAU_LOANS_OVERDUE feature appended to it.
    """
    # Create a series called HAS_CREDIT_BUREAU_LOANS_OVERDUE and fill it with zeros.  
    # Its index is identical to that of the main dataframe. It will eventually be appended 
    # to the main data frame as a column.
    HAS_CREDIT_BUREAU_LOANS_OVERDUE = pd.Series(data=0, index = dataframe['SK_ID_CURR'].index)

    # A list of all the borrowers IDs in the main dataframe
    main_data_table_borrower_IDs = dataframe['SK_ID_CURR'].values

    # For each loan in the bureau data table that is overdue 
    # (has a value for CREDIT_DAY_OVERDUE that's greater than 0)
    for index, row in bureau_data_filtered_for_overdue.iterrows():
        # The borrower ID (SK_ID_CURR) that owns the overdue loan
        borrower_ID = row['SK_ID_CURR']
        # If the borrower ID owning the overdue loan is also 
        # in the main data frame, then enter a value of 1 in 
        # the series HAS_CREDIT_BUREAU_LOANS_OVERDUE at an index 
        # that is identical to the index of the borrower ID 
        # in the main data frame.
        if borrower_ID in main_data_table_borrower_IDs:
            # The index of the borrower's row in the main data table.
            borrower_index_main_data_table = dataframe.index[dataframe['SK_ID_CURR'] == borrower_ID].tolist()[0]
            # Place a value of 1 at the index of the series HAS_CREDIT_BUREAU_LOANS_OVERDUE 
            # which corresponds to the index of the borrower's ID in the main data table.
            HAS_CREDIT_BUREAU_LOANS_OVERDUE.loc[borrower_index_main_data_table] = 1
    # Append the newly engineered HAS_CREDIT_BUREAU_LOANS_OVERDUE feature to the main dataframe.
    dataframe = dataframe.assign(HAS_CREDIT_BUREAU_LOANS_OVERDUE=HAS_CREDIT_BUREAU_LOANS_OVERDUE.values)
    return dataframe

# Build the HAS_CREDIT_BUREAU_LOANS_OVERDUE feature
X_train_raw = build_feature_HAS_CREDIT_BUREAU_LOANS_OVERDUE(X_train_raw)

# Add the new HAS_CREDIT_BUREAU_LOANS_OVERDUE feature to the list of binary categorical 
# features. There are now 34 of these features.
bin_cat_feat = bin_cat_feat + ['HAS_CREDIT_BUREAU_LOANS_OVERDUE']
In [589]:
# Find out what fraction of borrowers in the dataframe have overdue 
# loans from other lenders besides Home Credit.
num_borrowers_maindataframe = X_train_raw.shape[0]
num_borrowers_maindataframe_with_other_overdue_loans = X_train_raw[X_train_raw['HAS_CREDIT_BUREAU_LOANS_OVERDUE'] == 1].shape[0]
percent_borrowers_with_other_overdue_loans = round(num_borrowers_maindataframe_with_other_overdue_loans*100./num_borrowers_maindataframe, 2)
print('{} borrowers, or {}% of the training segment\'s {} borrowers have overdue loans from other lenders.'.format(num_borrowers_maindataframe_with_other_overdue_loans, percent_borrowers_with_other_overdue_loans, num_borrowers_maindataframe))
2693 borrowers, or 1.09% of the training segment's 246008 borrowers have overdue loans from other lenders.
In [590]:
# Step 9. Use the DAYS_EMPLOYED feature to engineer a binary categorical feature called HAS_JOB. 
# If the value of DAYS_EMPLOYED is 0 or less, then HAS_JOB will be 1. Otherwise, HAS_JOB will 
# be 0. This condition will apply to all borrowers who had a value of 365243 for DAYS_EMPLOYED, 
# which I hypothesized can be best interpreted as meaning that a borrower does not have a job.
DAYS_EMPLOYED_train = X_train_raw['DAYS_EMPLOYED']
HAS_JOB = DAYS_EMPLOYED_train.map(lambda x: 1 if x <= 0 else 0)

# Append the newly engineered FAMILY_SIZE feature to the main dataframe.
X_train_raw = X_train_raw.assign(HAS_JOB=HAS_JOB.values)
In [591]:
# Step 10. Drop the DAYS_EMPLOYED feature from the main dataframe
X_train_raw = X_train_raw.drop('DAYS_EMPLOYED', axis=1)

# Add the new HAS_JOB feature to the list of binary categorical features. 
# There are now 35 of these features.
bin_cat_feat = bin_cat_feat + ['HAS_JOB']
In [592]:
# Step 11. Translate the 2 non-normalized numerical features that have skewed distributions 
# and negative values: DAYS_REGISTRATION, and DAYS_LAST_PHONE_CHANGE

def translate_negative_valued_features(dataframe, feature_name_list):
    """
    Translate a dataset's continuous features containing several negative 
    values. The dataframe is modified such that all values of each feature 
    listed in the feature_name_list parameter become positive.

    Parameters:
        dataframe: Pandas dataframe containing the features
        feature_name_list: List of strings, containing the names 
                           of each feature whose values will be 
                           translated
    """
    for feature in feature_name_list:
        # The minimum, most-negative, value of the feature
        feature_min_value = dataframe[feature].min()
        # Translate each value of the feature in a positive direction, 
        # of magnitude that's equal to the feature's most negative value.
        dataframe[feature] = dataframe[feature].apply(lambda x: x - feature_min_value)

# Translate the above two negatively-valued features to positive values
translate_negative_valued_features(X_train_raw, non_norm_feat_neg_values_skewed)
In [593]:
# Step 12. Log-transform all 17 non-normalized numerical features that have skewed distributions. 
# These 17 features include the 2 that were translated to positive ranges in Step 11.

# Add the 2 features translated to positive ranges above in Step 11 to 
# the list of non-normalized skewed features with positive values. This is 
# the set of features that will be log-transformed
log_transform_feats = non_norm_feat_pos_values_skewed + non_norm_feat_neg_values_skewed

X_train_raw[log_transform_feats] = X_train_raw[log_transform_feats].apply(lambda x: np.log(x + 1))
In [594]:
# Step 13. Replace 'NaN' values for all numerical features with each feature's mean. Fit an imputer 
# to each numerical feature containing at least one 'NaN' entry.

# Create a list of all the 67 numerical features in the main dataframe. These include all 
# 17 features that were log-transformed in Step 12, as well as the 4 normal features that 
# still need to be scaled, as well as the 46 normal features that don't need scaling.
numerical_features = log_transform_feats + norm_feat_need_scaling + norm_feat_not_need_scaling

# Create a list of all numerical features in the training set that have at least one 'NaN' entry
numerical_features_with_nan = X_train_raw[numerical_features].columns[X_train_raw[numerical_features].isna().any()].tolist()

# Create an imputer
imputer = Imputer()
# Fit the imputer to each numerical feature in the training set that has 'NaN' values, 
# and replace each 'NaN' entry of each feature with that feature's mean.
X_train_raw[numerical_features_with_nan] = imputer.fit_transform(X_train_raw[numerical_features_with_nan])
In [595]:
# Step 14. Remove the borrower ID column, SK_ID_CURR, from the main dataframe
X_train_raw = X_train_raw.drop('SK_ID_CURR', axis=1)
In [596]:
# Verify that the main training dataframe has the expected number of columns. 
# Dataframe initially had 122 columns.
# 4 features have been added (HAS_CHILDREN, NUMBER_FAMILY_MEMBERS, HAS_CREDIT_BUREAU_LOANS_OVERDUE, HAS_JOB).
# 5 columns have been removed (TARGET, SK_ID_CURR, DAYS_EMPLOYED, CNT_CHILDREN, CNT_FAM_MEMBERS).
# Expected number of columns is thus 121.
print('The main training dataframe now has {} columns. Expected: 121.'.format(X_train_raw.shape[1]))
The main training dataframe now has 121 columns. Expected: 121.
In [597]:
# Step 15. One-hot encode all 19 non-binary categorical features.
X_train_raw = pd.get_dummies(X_train_raw, columns=cat_feat_need_one_hot)

# Create a list that includes only the newly one-hot encoded features 
# as well as all the categorical features that were already binary.
all_bin_cat_feat = X_train_raw.columns.tolist()
for column_name in X_train_raw[numerical_features].columns.tolist():
    all_bin_cat_feat.remove(column_name)
In [598]:
# Observe how many binary features now exist in the dataframe after one-hot encoding the 
# 19 non-binary categorical features.
print('After one-hot encoding, there are now {} binary features in the main training dataframe.'.format(len(all_bin_cat_feat)))
After one-hot encoding, there are now 184 binary features in the main training dataframe.
In [599]:
# Observe how many total columns now exist in the dataframe after one-hot encoding.
# It is expected there are 184 binary categorical features, and 67 scaled numerical features 
# for a total of 251 features.
print('After one-hot encoding, there are now {} columns in the main training dataframe. Expected: 251.'.format(X_train_raw.shape[1]))
After one-hot encoding, there are now 251 columns in the main training dataframe. Expected: 251.
In [600]:
# Step 16. Replace all 'NaN' values in all binary categorical features with 0.

# Create a list of binary categorical features with at least one 'NaN' entry
bin_cat_feat_with_nan = X_train_raw[all_bin_cat_feat].columns[X_train_raw[all_bin_cat_feat].isna().any()].tolist()

# Replace each 'NaN' value in each of these binary features with 0
X_train_raw[bin_cat_feat_with_nan] = X_train_raw[bin_cat_feat_with_nan].fillna(value=0)
In [601]:
# Step 17. Fit a min-max scaler to each of the 17 log-transformed numerical features, as well 
# as to the 4 features DAYS_BIRTH, DAYS_ID_PUBLISH, HOUR_APPR_PROCESS_START, and the normalized 
# feature REGION_POPULATION_RELATIVE. Each feature will be scaled to a range [0.0, 1.0].

# Build a list of all 21 features needing scaling. Add the list of features that 
# were log-normalized above in Step 12 to the list of normally shaped features 
# that need to be scaled to the range [0,1].
feats_to_scale = norm_feat_need_scaling + log_transform_feats

# Initialize a scaler with the default range of [0,1]
scaler = MinMaxScaler()

# Fit the scaler to each of the features of the train set that need to be scaled, 
# then transform each of these features' values to the new scale.
X_train_raw[feats_to_scale] = scaler.fit_transform(X_train_raw[feats_to_scale])

# Rename the dataframe to indicate that its columns have been fully preprocessed.
X_train_final = X_train_raw
In [602]:
# Indicate that training set preprocessing is done.
# Verify that the training dataframe has the expected number of columns. 
# It is expected there are 184 binary categorical features, 
# and 67 numerical features for a total of 251 features.
print('Training set preprocessing complete. The final training dataframe now has {} columns. Expected: 251.'.format(X_train_final.shape[1]))
Training set preprocessing complete. The final training dataframe now has 251 columns. Expected: 251.
In [603]:
# Step 18. Build a data preprocessing pipeline to used for all testing sets. 
# This pipeline will recreate all features that were engineered in the 
# training set during the original data preprocessing phase. 
# The pipeline will also apply the min-max scaling transforms 
# originally fit on features in the training set to all datapoints in a 
# testing set.

def adjust_columns_application_test_csv_table(testing_dataframe):
    """
    After it is one-hot encoded, application_test.csv data table will have one 
    extra column, 'REGION_RATING_CLIENT_W_CITY_-1', that is not present in the 
    training dataframe. This column will be removed from the testing datatable 
    in this case. Only 1 of the 48,744 rows in application_test.csv will have a 
    value of 1 for this feature following one-hot encoding. I am not worried 
    about this column's elimination from the testing dataframe affecting predictions.
    
    Additionally, unlike the test validation set, which originally comprised 20% of 
    application_train.csv, application_test.csv will be missing the following columns 
    after it is one-hot encoded:
    
    'CODE_GENDER_XNA', 'NAME_INCOME_TYPE_Maternity leave', 'NAME_FAMILY_STATUS_Unknown'
    
    In this case, we need to insert these columns into the testing dataframe, at 
    the exact same indices they are located at in the fully preprocessed training 
    dataframe.Each inserted column will be filled with all zeros. (If each of these 
    binary features are missing from the application_test.csv data table, we can infer 
    that each borrower in thatdata table obviously would have a 0 for each feature were 
    it present.)

    Parameters:
        testing_dataframe: Pandas dataframe containing the testing dataset
                           contained in the file application_test.csv
        
    Returns: a testing dataframe containing the exact same columns and 
             column order as found in the training dataframe
    """
    
    # Identify any columns in the one-hot encoded testing_dataframe that 
    # are not in X_train_raw. These columns will need to be removed from the
    # testing_dataframe. (Expected that there will only be one such 
    # column: 'REGION_RATING_CLIENT_W_CITY_-1')
    X_train_columns_list = X_train_raw.columns.tolist()
    testing_dataframe_columns_list = testing_dataframe.columns.tolist()
    for column_name in X_train_columns_list:
        if column_name in testing_dataframe_columns_list:
            testing_dataframe_columns_list.remove(column_name)
            columns_not_in_X_train_raw = testing_dataframe_columns_list
    
    # Drop any column from the testing_dataframe that is not in the 
    # training dataframe. Expected to only be the one column 'REGION_RATING_CLIENT_W_CITY_-1'
    for column in columns_not_in_X_train_raw:
        testing_dataframe = testing_dataframe.drop(column, axis=1)
    
    # Get the column indices of each of the features 'CODE_GENDER_XNA', 
    #'NAME_INCOME_TYPE_Maternity leave', 'NAME_FAMILY_STATUS_Unknown' from 
    # the raw training dataframe (X_train_raw) prior to having having PCA run on it.
    loc_code_gender_training_frame = X_train_raw.columns.get_loc('CODE_GENDER_XNA')
    loc_name_income_type_maternity_leave_training_frame = X_train_raw.columns.get_loc('NAME_INCOME_TYPE_Maternity leave')
    loc_name_family_status_unknown_training_frame = X_train_raw.columns.get_loc('NAME_FAMILY_STATUS_Unknown')

    # Insert each column into the testing dataframe at the same index it had 
    # in the X_train_raw dataframe before PCA was run. Fill each column with all 0s.
    # Order is important. 'CODE_GENDER_XNA' should be inserted first, followed by 
    # 'NAME_INCOME_TYPE_Maternity leave', and then finally 'NAME_FAMILY_STATUS_Unknown'.
    testing_dataframe.insert(loc=loc_code_gender_training_frame, column='CODE_GENDER_XNA', value=0)
    testing_dataframe.insert(loc=loc_name_income_type_maternity_leave_training_frame, column='NAME_INCOME_TYPE_Maternity leave', value=0)
    testing_dataframe.insert(loc=loc_name_family_status_unknown_training_frame, column='NAME_FAMILY_STATUS_Unknown', value=0)
    return testing_dataframe

def test_set_preprocessing_pipeline(testing_dataframe):
    """
    Recreate all features that were engineered in the training set during 
    the original data preprocessing phase. The pipeline will also apply 
    an imputer to the test data table fill 'NaN' values. Binary feature's 'Nan' 
    values will be filled with 0. The min-max scaler fit on features 
    in the training set will be applied to the numerical features in the testing set.

    Parameters:
        testing_dataframe: Pandas dataframe containing a testing dataset
        
    Returns: a fully preprocessed testing dataframe
    """
    
    # Create the HAS_CHILDREN feature.
    CNT_CHILDREN_test = testing_dataframe['CNT_CHILDREN']
    HAS_CHILDREN = CNT_CHILDREN_test.map(lambda x: 1 if x > 0 else 0)

    # Append the newly engineered HAS_CHILDREN feature to the main dataframe.
    testing_dataframe = testing_dataframe.assign(HAS_CHILDREN=HAS_CHILDREN.values)
    
    # Drop the CNT_CHILDREN column from the main dataframe
    testing_dataframe = testing_dataframe.drop('CNT_CHILDREN', axis=1)

    # Create the NUMBER_FAMILY_MEMBERS feature. 
    CNT_FAM_MEMBERS_test = testing_dataframe['CNT_FAM_MEMBERS']
    NUMBER_FAMILY_MEMBERS = CNT_FAM_MEMBERS_test.map(lambda x: 'one' if x == 1 else ('two' if x == 2 else 'three_plus'))

    # Append the newly engineered FAMILY_SIZE feature to the main dataframe.
    testing_dataframe = testing_dataframe.assign(NUMBER_FAMILY_MEMBERS=NUMBER_FAMILY_MEMBERS.values)
    
    # Drop the CNT_FAM_MEMBERS feature from the main dataframe
    testing_dataframe = testing_dataframe.drop('CNT_FAM_MEMBERS', axis=1)

    # Build the HAS_CREDIT_BUREAU_LOANS_OVERDUE feature
    testing_dataframe = build_feature_HAS_CREDIT_BUREAU_LOANS_OVERDUE(testing_dataframe)
    
    # Create the HAS_JOB feature
    DAYS_EMPLOYED_test = testing_dataframe['DAYS_EMPLOYED']
    HAS_JOB = DAYS_EMPLOYED_test.map(lambda x: 1 if x <= 0 else 0)

    # Append the newly engineered FAMILY_SIZE feature to the main dataframe.
    testing_dataframe = testing_dataframe.assign(HAS_JOB=HAS_JOB.values)
    
    # Drop the DAYS_EMPLOYED feature from the main dataframe
    testing_dataframe = testing_dataframe.drop('DAYS_EMPLOYED', axis=1)
    
    # Translate the two negatively-valued features DAYS_REGISTRATION, and 
    # DAYS_LAST_PHONE_CHANGE to positive values
    translate_negative_valued_features(testing_dataframe, non_norm_feat_neg_values_skewed)
    
    # Log-transform all 17 non-normalized numerical features that have skewed distributions.
    testing_dataframe[log_transform_feats] = testing_dataframe[log_transform_feats].apply(lambda x: np.log(x + 1))
    
    # Create a list of all numerical features in the testing dataframe that have at least one 'NaN' entry
    numerical_features_with_nan_testing = testing_dataframe[numerical_features].columns[testing_dataframe[numerical_features].isna().any()].tolist()
    
    # Use an imputer to replace 'NaN' values for all numerical features with each feature's mean.
    testing_dataframe[numerical_features_with_nan_testing] = imputer.fit_transform(testing_dataframe[numerical_features_with_nan_testing])
    
    # Remove the borrower ID column, SK_ID_CURR, from the main dataframe
    testing_dataframe = testing_dataframe.drop('SK_ID_CURR', axis=1)
    
    # One-hot encode all 19 non-binary categorical features.
    testing_dataframe = pd.get_dummies(testing_dataframe, columns=cat_feat_need_one_hot)

    # After one-hot encoding, the testing dataframe from application_test.csv will be 
    # missing 2 columns that are in the training dataframe. It will also have an extra 
    # column that was not in the training dataframe, giving it 249 total columns. 
    # If this is the case, we need to modify this testing dataframe so that its columns 
    # and column order is consistent with the training dataframe.
    if testing_dataframe.shape[1] == 249:
        testing_dataframe = adjust_columns_application_test_csv_table(testing_dataframe)
    
    # Create a list of the binary categorical features with at least one 'NaN' entry
    bin_cat_feat_with_nan_testing = testing_dataframe[all_bin_cat_feat].columns[testing_dataframe[all_bin_cat_feat].isna().any()].tolist()
    
    # Replace each 'NaN' value in each of these binary features with 0
    testing_dataframe[bin_cat_feat_with_nan_testing] = testing_dataframe[bin_cat_feat_with_nan_testing].fillna(value=0)
     
    # Transform each of the 21 features that need to be scaled to the range [0,1] using 
    # the min-max scaler fit on the training set.
    testing_dataframe[feats_to_scale] = scaler.transform(testing_dataframe[feats_to_scale])
    
    return testing_dataframe
In [604]:
# Step 19. Preprocess the test validation set.
X_test_final = test_set_preprocessing_pipeline(X_test_raw)
In [605]:
# Verify that the test validation dataframe has the expected number of columns after 
# preprocessing its data. It is expected there are 184 binary categorical features, 
# and 67 numerical features for a total of 251 features.
print('Test validation set preprocessing complete. The final test validation dataframe now has {} columns. Expected: 251.'.format(X_test_final.shape[1]))
Test validation set preprocessing complete. The final test validation dataframe now has 251 columns. Expected: 251.

V. Implementation

In [606]:
# Lists of of probability predictions and classifier names. 
# To be used to plot ROC curves of each classifier's prediction 
# probabilities.

y_score_list = []
clf_label_list = []
In [607]:
# Step 1. Create an ROC area-under-curve scorer.
def roc_auc_scorer(y_targets, y_score):
    """ 
    Calculates and returns the area under the ROC curve between 
    the true target values and the probability estimates of the 
    predicted values. 
    """
    # Calculate the performance score between 'y_true' and 'y_predict'
    score = roc_auc_score(y_targets, y_score)
    
    # Return the score
    return score
In [608]:
# Step 2. Use the Gaussian Naive Bayes classifier to make predictions on 
# the test validation set. Calculate the area under ROC curve score of 
# these predictions.

# Fit a Gaussian Naive Bayes classifier to the training dataframe.
clf_naive_bayes = GaussianNB()
clf_naive_bayes.fit(X_train_final, y_train)

# The Naive Bayes estimates of probability of the positive class (TARGET=1):   
# the probability estimate of each borrower making at least one late loan payment.
naive_bayes_y_score = clf_naive_bayes.predict_proba(X_test_final)[:, 1]

# The area under the ROC curve between the true target values and the 
# probability estimates of the predicted values.
naive_bayes_roc_auc_score = roc_auc_scorer(y_test, naive_bayes_y_score)

# Add the Naive Bayes classifier's scores to the results list.
y_score_list.append(naive_bayes_y_score)
clf_label_list.append('Naive Bayes All Features')

print('Naive Bayes (All Features) test validation set predictions\' ROC AUC score: {}'.format(naive_bayes_roc_auc_score))
Naive Bayes (All Features) test validation set predictions' ROC AUC score: 0.546645662333944
In [610]:
# Step 3. Create a method that performs GridSearchCV on a 
# AdaBoost classifier learning algorithm to discover the highest 
# scoring hyperparameter combination.
def find_best_hyperameters_adaboost(X_train, y_train):
    """ 
    Performs grid search over the 'n_estimators' parameter of an AdaBoost 
    classifier trained on the input data [X_train, y_train]. 
    """

    # Create an AdaBoost classifier object
    clf = AdaBoostClassifier()

    # Create a dictionary for the parameter 'n_estimators' with different values 
    # that will be attempted.
    params = {
        'learning_rate': [0.01, 0.1, 1.0],
        'n_estimators': [200, 250, 500,1000],
        'random_state': [42]
    }

    # Transform 'roc_auc_scorer' into a scoring function using 'make_scorer' 
    scoring_fnc = make_scorer(roc_auc_scorer)

    # Create a GridSearchCV object.
    grid = GridSearchCV(clf, params, scoring_fnc, cv=3)

    # Fit the grid search object to the data to compute the optimal model
    grid = grid.fit(X_train, y_train)

    # Return the optimal model after fitting the data
    return grid.best_estimator_

# The AdaBoost classifier with hyperparameter values that scored the best 
# in GridSearchCV. 
clf_AdaBoost = find_best_hyperameters_adaboost(X_train_final, y_train)

print('Highest scoring AdaBoost classifier after running GridSearchCV: {}'.format(clf_AdaBoost))
Highest scoring AdaBoost classifier after running GridSearchCV: AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None,
          learning_rate=1.0, n_estimators=1000, random_state=42)
In [611]:
# Step 4. Use the AdaBoost classifier to make predictions on 
# the test validation set. Calculate the area under ROC curve score of 
# these predictions.

# The AdaBoost classifier's estimates of probability of the positive class (TARGET=1):  
# the probability estimate of each borrower making at least one late loan payment.
adaBoost_y_score = clf_AdaBoost.predict_proba(X_test_final)[:, 1]

# The area under the ROC curve between the true target values and the 
# probability estimates of the predicted values.
adaBoost_roc_auc_score = roc_auc_scorer(y_test, adaBoost_y_score)

# Add the AdaBoost classifier's scores to the results list.
y_score_list.append(adaBoost_y_score)
clf_label_list.append('AdaBoost All Features')

print('AdaBoost (All Features) test validation set predictions\' ROC AUC score: {}'.format(adaBoost_roc_auc_score))
AdaBoost (All Features) test validation set predictions' ROC AUC score: 0.7462758964509755
In [612]:
# Determine and display feature importances as determined by the AdaBoost classifier after 
# it was fit on the full featureset.

feature_list = X_train_final.columns.values
feature_importance_list = clf_AdaBoost.feature_importances_
rows_list = []
for i in range(len(feature_importance_list)):
    if feature_importance_list[i] > 0:
        dictionary = {}
        dictionary['Feature Name'] = feature_list[i]
        dictionary['Importance'] = feature_importance_list[i]
        rows_list.append(dictionary)
    
adaBoost_feature_importances = pd.DataFrame(rows_list, columns=['Feature Name', 'Importance'])
adaBoost_feature_importances = adaBoost_feature_importances.sort_values('Importance',ascending=False)
    
display(adaBoost_feature_importances)
Feature Name Importance
14 EXT_SOURCE_1 0.075
15 EXT_SOURCE_2 0.073
1 AMT_CREDIT 0.066
2 AMT_ANNUITY 0.063
5 DAYS_BIRTH 0.061
16 EXT_SOURCE_3 0.052
3 AMT_GOODS_PRICE 0.046
7 DAYS_ID_PUBLISH 0.034
61 DAYS_LAST_PHONE_CHANGE 0.032
56 TOTALAREA_MODE 0.027
6 DAYS_REGISTRATION 0.027
0 AMT_INCOME_TOTAL 0.022
31 APARTMENTS_MODE 0.017
35 COMMONAREA_MODE 0.015
28 LIVINGAREA_AVG 0.013
40 LANDAREA_MODE 0.012
30 NONLIVINGAREA_AVG 0.012
32 BASEMENTAREA_MODE 0.011
48 COMMONAREA_MEDI 0.011
4 REGION_POPULATION_RELATIVE 0.011
42 LIVINGAREA_MODE 0.010
21 COMMONAREA_AVG 0.010
26 LANDAREA_AVG 0.009
8 OWN_CAR_AGE 0.009
27 LIVINGAPARTMENTS_AVG 0.009
54 LIVINGAREA_MEDI 0.009
11 HOUR_APPR_PROCESS_START 0.009
44 NONLIVINGAREA_MODE 0.008
45 APARTMENTS_MEDI 0.007
33 YEARS_BEGINEXPLUATATION_MODE 0.007
77 AMT_REQ_CREDIT_BUREAU_MON 0.007
18 BASEMENTAREA_AVG 0.006
25 FLOORSMIN_AVG 0.005
78 AMT_REQ_CREDIT_BUREAU_QRT 0.005
47 YEARS_BEGINEXPLUATATION_MEDI 0.005
46 BASEMENTAREA_MEDI 0.005
17 APARTMENTS_AVG 0.005
34 YEARS_BUILD_MODE 0.005
53 LIVINGAPARTMENTS_MEDI 0.005
20 YEARS_BUILD_AVG 0.005
29 NONLIVINGAPARTMENTS_AVG 0.005
38 FLOORSMAX_MODE 0.004
24 FLOORSMAX_AVG 0.004
41 LIVINGAPARTMENTS_MODE 0.004
75 AMT_REQ_CREDIT_BUREAU_DAY 0.004
19 YEARS_BEGINEXPLUATATION_AVG 0.004
79 AMT_REQ_CREDIT_BUREAU_YEAR 0.004
59 OBS_60_CNT_SOCIAL_CIRCLE 0.004
156 WALLSMATERIAL_MODE_Panel 0.003
83 CODE_GENDER_M 0.003
58 DEF_30_CNT_SOCIAL_CIRCLE 0.003
51 FLOORSMIN_MEDI 0.003
76 AMT_REQ_CREDIT_BUREAU_WEEK 0.003
52 LANDAREA_MEDI 0.003
88 NAME_INCOME_TYPE_State servant 0.003
50 ENTRANCES_MEDI 0.003
39 FLOORSMIN_MODE 0.003
37 ENTRANCES_MODE 0.003
92 NAME_EDUCATION_TYPE_Higher education 0.003
36 ELEVATORS_MODE 0.003
23 ENTRANCES_AVG 0.002
144 OCCUPATION_TYPE_Core staff 0.002
71 FLAG_DOCUMENT_15 0.002
9 FLAG_WORK_PHONE 0.002
22 ELEVATORS_AVG 0.002
95 NAME_FAMILY_STATUS_Married 0.002
130 ORGANIZATION_TYPE_Self-employed 0.002
60 DEF_60_CNT_SOCIAL_CIRCLE 0.002
49 ELEVATORS_MEDI 0.002
108 ORGANIZATION_TYPE_Construction 0.002
43 NONLIVINGAPARTMENTS_MODE 0.002
129 ORGANIZATION_TYPE_Security Ministries 0.001
131 ORGANIZATION_TYPE_Trade: type 2 0.001
132 ORGANIZATION_TYPE_Trade: type 3 0.001
157 FONDKAPREMONT_MODE_org spec account 0.001
128 ORGANIZATION_TYPE_School 0.001
127 ORGANIZATION_TYPE_Restaurant 0.001
126 ORGANIZATION_TYPE_Realtor 0.001
125 ORGANIZATION_TYPE_Postal 0.001
124 ORGANIZATION_TYPE_Police 0.001
123 ORGANIZATION_TYPE_Mobile 0.001
122 ORGANIZATION_TYPE_Military 0.001
121 ORGANIZATION_TYPE_Legal Services 0.001
120 ORGANIZATION_TYPE_Insurance 0.001
119 ORGANIZATION_TYPE_Industry: type 9 0.001
118 ORGANIZATION_TYPE_Industry: type 5 0.001
117 ORGANIZATION_TYPE_Industry: type 3 0.001
134 ORGANIZATION_TYPE_Trade: type 6 0.001
116 ORGANIZATION_TYPE_Industry: type 12 0.001
133 ORGANIZATION_TYPE_Trade: type 5 0.001
153 WALLSMATERIAL_MODE_Block 0.001
135 ORGANIZATION_TYPE_Trade: type 7 0.001
136 ORGANIZATION_TYPE_Transport: type 2 0.001
158 FONDKAPREMONT_MODE_reg oper spec account 0.001
155 WALLSMATERIAL_MODE_Others 0.001
114 ORGANIZATION_TYPE_Industry: type 1 0.001
154 WALLSMATERIAL_MODE_Monolithic 0.001
152 HOUSETYPE_MODE_terraced house 0.001
151 OCCUPATION_TYPE_Security staff 0.001
150 OCCUPATION_TYPE_Sales staff 0.001
149 OCCUPATION_TYPE_Medicine staff 0.001
148 OCCUPATION_TYPE_Low-skill Laborers 0.001
147 OCCUPATION_TYPE_Laborers 0.001
146 OCCUPATION_TYPE_High skill tech staff 0.001
145 OCCUPATION_TYPE_Drivers 0.001
143 OCCUPATION_TYPE_Cooking staff 0.001
142 OCCUPATION_TYPE_Cleaning staff 0.001
141 OCCUPATION_TYPE_Accountants 0.001
140 NAME_TYPE_SUITE_Spouse, partner 0.001
139 NAME_TYPE_SUITE_Children 0.001
138 ORGANIZATION_TYPE_Transport: type 4 0.001
137 ORGANIZATION_TYPE_Transport: type 3 0.001
115 ORGANIZATION_TYPE_Industry: type 10 0.001
80 HAS_CREDIT_BUREAU_LOANS_OVERDUE 0.001
113 ORGANIZATION_TYPE_Housing 0.001
67 FLAG_DOCUMENT_8 0.001
84 FLAG_OWN_CAR_N 0.001
82 NAME_CONTRACT_TYPE_Revolving loans 0.001
81 NAME_CONTRACT_TYPE_Cash loans 0.001
74 FLAG_DOCUMENT_18 0.001
73 FLAG_DOCUMENT_17 0.001
72 FLAG_DOCUMENT_16 0.001
70 FLAG_DOCUMENT_14 0.001
69 FLAG_DOCUMENT_13 0.001
68 FLAG_DOCUMENT_11 0.001
66 FLAG_DOCUMENT_6 0.001
112 ORGANIZATION_TYPE_Hotel 0.001
65 FLAG_DOCUMENT_5 0.001
64 FLAG_DOCUMENT_4 0.001
63 FLAG_DOCUMENT_3 0.001
62 FLAG_DOCUMENT_2 0.001
57 OBS_30_CNT_SOCIAL_CIRCLE 0.001
55 NONLIVINGAREA_MEDI 0.001
13 REG_CITY_NOT_LIVE_CITY 0.001
12 REG_REGION_NOT_LIVE_REGION 0.001
10 FLAG_PHONE 0.001
85 FLAG_OWN_REALTY_N 0.001
86 NAME_INCOME_TYPE_Commercial associate 0.001
87 NAME_INCOME_TYPE_Maternity leave 0.001
89 NAME_INCOME_TYPE_Student 0.001
111 ORGANIZATION_TYPE_Government 0.001
110 ORGANIZATION_TYPE_Emergency 0.001
109 ORGANIZATION_TYPE_Electricity 0.001
107 ORGANIZATION_TYPE_Business Entity Type 3 0.001
106 ORGANIZATION_TYPE_Bank 0.001
105 WEEKDAY_APPR_PROCESS_START_WEDNESDAY 0.001
104 WEEKDAY_APPR_PROCESS_START_SUNDAY 0.001
103 WEEKDAY_APPR_PROCESS_START_SATURDAY 0.001
102 WEEKDAY_APPR_PROCESS_START_MONDAY 0.001
101 REGION_RATING_CLIENT_W_CITY_3 0.001
100 REGION_RATING_CLIENT_W_CITY_1 0.001
99 NAME_HOUSING_TYPE_Rented apartment 0.001
98 NAME_HOUSING_TYPE_Office apartment 0.001
97 NAME_HOUSING_TYPE_Municipal apartment 0.001
96 NAME_FAMILY_STATUS_Widow 0.001
94 NAME_EDUCATION_TYPE_Secondary / secondary special 0.001
93 NAME_EDUCATION_TYPE_Incomplete higher 0.001
91 NAME_EDUCATION_TYPE_Academic degree 0.001
90 NAME_INCOME_TYPE_Unemployed 0.001
159 NUMBER_FAMILY_MEMBERS_two 0.001

160 rows × 2 columns

In [613]:
# Step 5. Try using a Logistic Regression classifier to make predictions.

# Fit the classifier to the training data.
clf_logistic_regression = LogisticRegression(penalty='l2', random_state=42, solver='liblinear')
clf_logistic_regression.fit(X_train_final, y_train)

# The logistical regression classifier's estimates of probability of the positive 
# class (TARGET=1): the probability estimate of each borrower making at least one 
# late loan payment.
logistic_regression_y_score = clf_logistic_regression.predict_proba(X_test_final)[:, 1]

# The area under the ROC curve between the true target values and the 
# probability estimates of the predicted values.
logistic_regression_roc_auc_score = roc_auc_scorer(y_test, logistic_regression_y_score)

# Add the Logistic Regression classifier's scores to the results list.
y_score_list.append(logistic_regression_y_score)
clf_label_list.append('Logistic Regression All Features')

print('Logistic Regression (All Features) test validation set predictions\' ROC AUC score: {}'.format(logistic_regression_roc_auc_score))
Logistic Regression (All Features) test validation set predictions' ROC AUC score: 0.7471756350178691
In [614]:
# Step 6. Try using a Multi-layer Perceptron classifier to make predictions.

# Fit the classifier to the training data.
clf_mlp = MLPClassifier(
    hidden_layer_sizes=100, activation='identity', solver='adam', alpha=0.001, batch_size=200, 
    learning_rate_init=0.001, random_state=42
)
clf_mlp.fit(X_train_final, y_train)

# The multi-layer perceptron classifier's estimates of probability of the positive 
# class (TARGET=1): the probability estimate of each borrower making at least one 
# late loan payment.
mlp_y_score = clf_mlp.predict_proba(X_test_final)[:, 1]

# The area under the ROC curve between the true target values and the 
# probability estimates of the predicted values.
mlp_roc_auc_score = roc_auc_scorer(y_test, mlp_y_score)

# Add the Multi-Layer Perceptron classifier's scores to the results list.
y_score_list.append(mlp_y_score)
clf_label_list.append('Multi-Layer Perceptron All Features')

print('Multi-layer Perceptron (All Features) test validation set predictions\' ROC AUC score: {}'.format(mlp_roc_auc_score))
Multi-layer Perceptron (All Features) test validation set predictions' ROC AUC score: 0.7429017839300756
In [615]:
# Step 7. Try using a LightGBM classifier.

# Convert preprocessed training dataset into LightGBM dataset format
lightgbm_training = lgb.Dataset(X_train_final, label=y_train)

# Specify parameters
params = {}
params['learning_rate'] = 0.01
params['boosting_type'] = 'gbdt'
params['objective'] = 'binary'
params['metric'] = 'auc'
params['sub_feature'] = 0.3
params['num_leaves'] = 100
params['min_data_in_leaf'] = 500
params['max_depth'] = 10
params['max_bin'] = 64
#params['min_data_in_bin'] = 3
#params['lambda_l1'] = 0.01
params['lambda_l2'] = 0.01
#params['min_gain_to_split'] = 0.01
params['bagging_freq'] = 100
params['bagging_fraction'] = 0.9
#params['feature_fraction'] = 0.5

# Fit the LightGBM classifier to the training data
clf_lgb = lgb.train(params, lightgbm_training, 1500)

# Classifier's estimates of probability of the positive class (TARGET=1): the 
# probability estimate of each borrower making at least one late loan payment.
lgb_y_score = clf_lgb.predict(X_test_final)

# The area under the ROC curve between the true target values and the 
# probability estimates of the predicted values.
lgb_roc_auc_score = roc_auc_scorer(y_test, lgb_y_score)

# Add the LightGBM classifier's scores to the results list.
y_score_list.append(lgb_y_score)
clf_label_list.append('LightGBM All Features')

print('LightGBM (All Features) test validation set predictions\' ROC AUC score: {}'.format(lgb_roc_auc_score))
LightGBM (All Features) test validation set predictions' ROC AUC score: 0.7592132612569703
In [243]:
# Step 7. Build a prediction pipeline for the testing data table (application_test.csv) that 
# saves prediction probabilities to a CSV file, which will then be submitted on Kaggle.

# def testing_data_table_predictions_to_csv(clf, testing_data_table, isLightGBM):
#     """
#     A prediction pipeline that:
#     1. Preprocesses the 48,744 row testing data table 
#     2. Uses a classifier to compute estimates of the probability of the positive 
#        class (TARGET=1) for each borrower: the probability estimate of each borrower 
#        making at least one late loan payment.
#     3. Saves a CSV file that contains probabilities of target labels for each 
#        borrower (SK_ID_CURR) in the testing data table.
#     4. isLightGBM: Boolean, a flag that indicates whether or not the classifier is 
#                    LightGBM. If True, 

#     Parameters:
#         clf: A machine learning classifier object that has already been fit to 
#                     the training data.
#         testing_data_table: Pandas dataframe containing the testing dataset.
#     """
    
#     # Get a list of the borrower IDs (SK_ID_CURR column). The borrower ID must be 
#     # placed in each row of CSV file that will be created.
#     borrower_IDs = testing_data_table['SK_ID_CURR']
    
#     # Preprocess the testing data table so that predictions can be made on it.
#     X_test_final = test_set_preprocessing_pipeline(testing_data_table)
#     #print('application_test.csv testing set processing complete. The processed dataframe now has {} columns. Expected: 251.'.format(X_test_final.shape[1]))
    
#     # Classifier's estimates of probability of the positive class (TARGET=1): the 
#     # probability estimate of each borrower making at least one late loan payment. 
#     # If classifier is LightGBM, the method for making predictions is merely 'predict'
#     # and the arrray containing these probabilities has slightly different shape than 
#     # those produced by the other classifiers.
#     if isLightGBM:
#         clf_y_score = clf.predict(X_test_final)
#     else:
#         clf_y_score = clf.predict_proba(X_test_final)[:, 1]
    
#     # Create the CSV file that will be saved
#     file_output = 'dellinger_kaggle_home_credit_submission2.csv'
#     # Write to the CSV file
#     with open(file_output, 'w') as csvfile:
#         writer = csv.writer(csvfile)
#         # Write the header row
#         writer.writerow(['SK_ID_CURR','TARGET'])
#         # Write a row for each borrower that contains the 
#         # prediction probability of their label.
#         for index, value in borrower_IDs.iteritems():
#             writer.writerow([value, clf_y_score[index]])

# # To submit to Kaggle: the LightGBM Classifier's predictions on full featureset.
# # Create predictions on the data in the testing data table (application_test.csv) 
# # using the LightGBM classifier fit above in Step 5. Also create a CSV 
# # file containing the prediction probabilities for each borrower ID (SK_ID_CURR) 
# # in the testing data table.
# testing_data_table_predictions_to_csv(clf_lgb, application_test_data, True)
application_test.csv testing set processing complete. The processed dataframe now has 251 columns. Expected: 251.

VI. Refinement

PCA Reduce Numeric Features

In [616]:
# Step 1. Now try training all classifiers on a featureset where PCA is used to compress the 
# dimensions of the 67 numerical features.

# Load the main data tables
application_train_data = pd.read_csv("data/application_train.csv")
application_test_data = pd.read_csv("data/application_test.csv")

# Load the Bureau data table
bureau_data = pd.read_csv("data/bureau.csv")

# 1: Create lists of different feature types in the main data 
# frame, based on how each type will need to be preprocessed.

# i. All 18 categorical features needing one-hot encoding. 
#    Includes the 4 categorical features originally 
#    mis-identified as having been normalized:
#    EMERGENCYSTATE_MODE, HOUSETYPE_MODE, WALLSMATERIAL_MODE, 
#    FONDKAPREMONT_MODE
cat_feat_need_one_hot = [
    'NAME_CONTRACT_TYPE', 'CODE_GENDER', 'FLAG_OWN_CAR', 
    'FLAG_OWN_REALTY', 'NAME_INCOME_TYPE', 'NAME_EDUCATION_TYPE', 
    'NAME_FAMILY_STATUS', 'NAME_HOUSING_TYPE', 'REGION_RATING_CLIENT',
    'REGION_RATING_CLIENT_W_CITY', 'WEEKDAY_APPR_PROCESS_START', 'ORGANIZATION_TYPE',
    'NAME_TYPE_SUITE', 'OCCUPATION_TYPE', 'EMERGENCYSTATE_MODE',
    'HOUSETYPE_MODE', 'WALLSMATERIAL_MODE', 'FONDKAPREMONT_MODE'
]

# ii. All 32 binary categorical features already one-hot encoded.
bin_cat_feat = [
    'FLAG_MOBIL', 'FLAG_EMP_PHONE', 'FLAG_WORK_PHONE',
    'FLAG_CONT_MOBILE', 'FLAG_PHONE', 'FLAG_EMAIL',
    'REG_REGION_NOT_LIVE_REGION', 'REG_REGION_NOT_WORK_REGION', 'LIVE_REGION_NOT_WORK_REGION',
    'REG_CITY_NOT_LIVE_CITY', 'REG_CITY_NOT_WORK_CITY', 'LIVE_CITY_NOT_WORK_CITY',
    'FLAG_DOCUMENT_2', 'FLAG_DOCUMENT_3', 'FLAG_DOCUMENT_4',
    'FLAG_DOCUMENT_5', 'FLAG_DOCUMENT_6', 'FLAG_DOCUMENT_7',
    'FLAG_DOCUMENT_8', 'FLAG_DOCUMENT_9', 'FLAG_DOCUMENT_10',
    'FLAG_DOCUMENT_11', 'FLAG_DOCUMENT_12', 'FLAG_DOCUMENT_13',
    'FLAG_DOCUMENT_14', 'FLAG_DOCUMENT_15', 'FLAG_DOCUMENT_16',
    'FLAG_DOCUMENT_17', 'FLAG_DOCUMENT_18', 'FLAG_DOCUMENT_19',
    'FLAG_DOCUMENT_20', 'FLAG_DOCUMENT_21'
]

# iii. All 2 non-normalized numerical features with skewed distributions 
#    and negative values. These features will need to have their 
#    distributions translated to positive ranges before being 
#    log-transformed, and then later scaled to the range [0,1].
non_norm_feat_neg_values_skewed = [
    'DAYS_REGISTRATION', 'DAYS_LAST_PHONE_CHANGE'
]


# iv. All 15 non-normalized numerical features with skewed distributions, 
#    and only positive values. These features will need to be 
#    log-transformed, and eventually scaled to the range [0,1].
non_norm_feat_pos_values_skewed = [
    'AMT_INCOME_TOTAL', 'AMT_CREDIT', 'AMT_ANNUITY',
    'AMT_GOODS_PRICE', 'OBS_30_CNT_SOCIAL_CIRCLE', 'DEF_30_CNT_SOCIAL_CIRCLE',
    'OBS_60_CNT_SOCIAL_CIRCLE', 'DEF_60_CNT_SOCIAL_CIRCLE', 'AMT_REQ_CREDIT_BUREAU_HOUR',
    'AMT_REQ_CREDIT_BUREAU_DAY', 'AMT_REQ_CREDIT_BUREAU_WEEK', 'AMT_REQ_CREDIT_BUREAU_MON',
    'AMT_REQ_CREDIT_BUREAU_QRT', 'AMT_REQ_CREDIT_BUREAU_YEAR', 'OWN_CAR_AGE'
]

# v. All 4 numerical features with normal shapes but needing to be scaled 
#    to the range [0,1].
norm_feat_need_scaling = [
    'DAYS_BIRTH', 'DAYS_ID_PUBLISH', 'HOUR_APPR_PROCESS_START',
    'REGION_POPULATION_RELATIVE'
]

# vi. All 46 numerical features that have been normalized to the range 
#   [0,1]. These features will need neither log-transformation, nor 
#   any further scaling.
norm_feat_not_need_scaling = [
    'EXT_SOURCE_2', 'EXT_SOURCE_3', 'YEARS_BEGINEXPLUATATION_AVG',
    'YEARS_BEGINEXPLUATATION_MODE', 'YEARS_BEGINEXPLUATATION_MEDI', 'FLOORSMAX_AVG', 
    'FLOORSMAX_MODE', 'FLOORSMAX_MEDI', 'LIVINGAREA_AVG',
    'LIVINGAREA_MODE', 'LIVINGAREA_MEDI', 'ENTRANCES_AVG',
    'ENTRANCES_MODE', 'ENTRANCES_MEDI', 'APARTMENTS_AVG',
    'APARTMENTS_MODE', 'APARTMENTS_MEDI', 'ELEVATORS_AVG', 
    'ELEVATORS_MODE', 'ELEVATORS_MEDI', 'NONLIVINGAREA_AVG',
    'NONLIVINGAREA_MODE', 'NONLIVINGAREA_MEDI', 'EXT_SOURCE_1',
    'BASEMENTAREA_AVG', 'BASEMENTAREA_MODE', 'BASEMENTAREA_MEDI',
    'LANDAREA_AVG', 'LANDAREA_MODE', 'LANDAREA_MEDI',
    'YEARS_BUILD_AVG', 'YEARS_BUILD_MODE', 'YEARS_BUILD_MEDI',
    'FLOORSMIN_AVG', 'FLOORSMIN_MODE', 'FLOORSMIN_MEDI',
    'LIVINGAPARTMENTS_AVG', 'LIVINGAPARTMENTS_MODE', 'LIVINGAPARTMENTS_MEDI',
    'NONLIVINGAPARTMENTS_AVG', 'NONLIVINGAPARTMENTS_MODE', 'NONLIVINGAPARTMENTS_MEDI',
    'COMMONAREA_AVG', 'COMMONAREA_MODE', 'COMMONAREA_MEDI',
    'TOTALAREA_MODE'
]

# vii. The remaining 3 features in the main data frame that will be 
#    re-engineered and transformed into different features
feat_to_be_reengineered = [
    'CNT_CHILDREN', 'CNT_FAM_MEMBERS', 'DAYS_EMPLOYED'
]

# 2: Separate target data from training dataset.
targets = application_train_data['TARGET']
features_raw = application_train_data.drop('TARGET', axis = 1)

# 3: Use train_test_split from sklearn.cross_validation to 
# create a test validation set that is 20% of the size of the total training set: 
# Will allow me to compare performance of various learning algorithms without 
# overfitting to the training data.
X_train_raw, X_test_raw, y_train, y_test = train_test_split(features_raw, 
                                                    targets, 
                                                    test_size = 0.2, 
                                                    random_state = 42)

# 4: Use the CNT_CHILDREN feature to engineer a binary 
# categorical feature called HAS_CHILDREN. If value of CNT_CHILDREN is 
# greater than 0, the value of HAS_CHILDREN will be 1. If value of CNT_CHILDREN is 
# 0, value of HAS_CHILDREN will be 0. 
CNT_CHILDREN_train = X_train_raw['CNT_CHILDREN']
HAS_CHILDREN = CNT_CHILDREN_train.map(lambda x: 1 if x > 0 else 0)

# Append the newly engineered HAS_CHILDREN feature to the main dataframe.
X_train_raw = X_train_raw.assign(HAS_CHILDREN=HAS_CHILDREN.values)

# 5: Drop the CNT_CHILDREN column from the main dataframe
X_train_raw = X_train_raw.drop('CNT_CHILDREN', axis=1)

# Add the new HAS_CHILDREN feature to the list of binary categorical 
# features that are already one-hot encoded. There are now 33 such features.
bin_cat_feat = bin_cat_feat + ['HAS_CHILDREN']

# 6. Use the CNT_FAM_MEMBERS feature to engineer a categorical feature called NUMBER_FAMILY_MEMBERS. 
# If CNT_FAM_MEMBERS is 1.0, then the value of NUMBER_FAMILY_MEMBERS will be 'one'. If CNT_FAM_MEMBERS is 2.0, 
# then NUMBER_FAMILY_MEMBERS will be 'two'. If CNT_FAM_MEMBERS is 3.0 or greater, then NUMBER_FAMILY_MEMBERS will 
# be 'three_plus'.
CNT_FAM_MEMBERS_train = X_train_raw['CNT_FAM_MEMBERS']
NUMBER_FAMILY_MEMBERS = CNT_FAM_MEMBERS_train.map(lambda x: 'one' if x == 1 else ('two' if x == 2 else 'three_plus'))

# Append the newly engineered FAMILY_SIZE feature to the main dataframe.
X_train_raw = X_train_raw.assign(NUMBER_FAMILY_MEMBERS=NUMBER_FAMILY_MEMBERS.values)

# 7. Drop the CNT_FAM_MEMBERS feature from the main dataframe
X_train_raw = X_train_raw.drop('CNT_FAM_MEMBERS', axis=1)

# Add the new NUMBER_FAMILY_MEMBERS feature to the list of categorical 
# features that will need to be one-hot encoded. There are now 19 of these features.
cat_feat_need_one_hot = cat_feat_need_one_hot + ['NUMBER_FAMILY_MEMBERS']

# 8. Use the CREDIT_DAY_OVERDUE feature in bureau.csv to engineer the binary 
# categorical HAS_CREDIT_BUREAU_LOANS_OVERDUE feature. If CREDIT_DAY_OVERDUE for a 
# particular borrower ID (SK_ID_CURR) is greater than 0, then the value of 
# HAS_CREDIT_BUREAU_LOANS_OVERDUE will be 1. If CREDIT_DAY_OVERDUE for a particular 
# borrower ID is 0, then the value of HAS_CREDIT_BUREAU_LOANS_OVERDUE will be 0.

# Filter the bureau data table for loans which are overdue (have a value 
# for CREDIT_DAY_OVERDUE that's greater than 0)
bureau_data_filtered_for_overdue = bureau_data[bureau_data['CREDIT_DAY_OVERDUE'] > 0]

def build_feature_HAS_CREDIT_BUREAU_LOANS_OVERDUE(dataframe):
    """
    Use the CREDIT_DAY_OVERDUE feature in bureau.csv to engineer the binary 
    categorical HAS_CREDIT_BUREAU_LOANS_OVERDUE feature. If CREDIT_DAY_OVERDUE for a 
    particular borrower ID (SK_ID_CURR) is greater than 0, then the value of 
    HAS_CREDIT_BUREAU_LOANS_OVERDUE will be 1. If CREDIT_DAY_OVERDUE for a particular 
    borrower ID is 0, then the value of HAS_CREDIT_BUREAU_LOANS_OVERDUE will be 0.

    Parameters:
        dataframe: Pandas dataframe containing a training or testing dataset
        
    Returns: The dataframe with HAS_CREDIT_BUREAU_LOANS_OVERDUE feature appended to it.
    """
    # Create a series called HAS_CREDIT_BUREAU_LOANS_OVERDUE and fill it with zeros.  
    # Its index is identical to that of the main dataframe. It will eventually be appended 
    # to the main data frame as a column.
    HAS_CREDIT_BUREAU_LOANS_OVERDUE = pd.Series(data=0, index = dataframe['SK_ID_CURR'].index)

    # A list of all the borrowers IDs in the main dataframe
    main_data_table_borrower_IDs = dataframe['SK_ID_CURR'].values

    # For each loan in the bureau data table that is overdue 
    # (has a value for CREDIT_DAY_OVERDUE that's greater than 0)
    for index, row in bureau_data_filtered_for_overdue.iterrows():
        # The borrower ID (SK_ID_CURR) that owns the overdue loan
        borrower_ID = row['SK_ID_CURR']
        # If the borrower ID owning the overdue loan is also 
        # in the main data frame, then enter a value of 1 in 
        # the series HAS_CREDIT_BUREAU_LOANS_OVERDUE at an index 
        # that is identical to the index of the borrower ID 
        # in the main data frame.
        if borrower_ID in main_data_table_borrower_IDs:
            # The index of the borrower's row in the main data table.
            borrower_index_main_data_table = dataframe.index[dataframe['SK_ID_CURR'] == borrower_ID].tolist()[0]
            # Place a value of 1 at the index of the series HAS_CREDIT_BUREAU_LOANS_OVERDUE 
            # which corresponds to the index of the borrower's ID in the main data table.
            HAS_CREDIT_BUREAU_LOANS_OVERDUE.loc[borrower_index_main_data_table] = 1
    # Append the newly engineered HAS_CREDIT_BUREAU_LOANS_OVERDUE feature to the main dataframe.
    dataframe = dataframe.assign(HAS_CREDIT_BUREAU_LOANS_OVERDUE=HAS_CREDIT_BUREAU_LOANS_OVERDUE.values)
    return dataframe

# Build the HAS_CREDIT_BUREAU_LOANS_OVERDUE feature
X_train_raw = build_feature_HAS_CREDIT_BUREAU_LOANS_OVERDUE(X_train_raw)

# Add the new HAS_CREDIT_BUREAU_LOANS_OVERDUE feature to the list of binary categorical 
# features. There are now 34 of these features.
bin_cat_feat = bin_cat_feat + ['HAS_CREDIT_BUREAU_LOANS_OVERDUE']

# 9. Use the DAYS_EMPLOYED feature to engineer a binary categorical feature called HAS_JOB. 
# If the value of DAYS_EMPLOYED is 0 or less, then HAS_JOB will be 1. Otherwise, HAS_JOB will 
# be 0. This condition will apply to all borrowers who had a value of 365243 for DAYS_EMPLOYED, 
# which I hypothesized can be best interpreted as meaning that a borrower does not have a job.
DAYS_EMPLOYED_train = X_train_raw['DAYS_EMPLOYED']
HAS_JOB = DAYS_EMPLOYED_train.map(lambda x: 1 if x <= 0 else 0)

# Append the newly engineered FAMILY_SIZE feature to the main dataframe.
X_train_raw = X_train_raw.assign(HAS_JOB=HAS_JOB.values)

# 10. Drop the DAYS_EMPLOYED feature from the main dataframe
X_train_raw = X_train_raw.drop('DAYS_EMPLOYED', axis=1)

# Add the new HAS_JOB feature to the list of binary categorical features. 
# There are now 35 of these features.
bin_cat_feat = bin_cat_feat + ['HAS_JOB']

# 11. Translate the 2 non-normalized numerical features that have skewed distributions 
# and negative values: DAYS_REGISTRATION, and DAYS_LAST_PHONE_CHANGE

def translate_negative_valued_features(dataframe, feature_name_list):
    """
    Translate a dataset's continuous features containing several negative 
    values. The dataframe is modified such that all values of each feature 
    listed in the feature_name_list parameter become positive.

    Parameters:
        dataframe: Pandas dataframe containing the features
        feature_name_list: List of strings, containing the names 
                           of each feature whose values will be 
                           translated
    """
    for feature in feature_name_list:
        # The minimum, most-negative, value of the feature
        feature_min_value = dataframe[feature].min()
        # Translate each value of the feature in a positive direction, 
        # of magnitude that's equal to the feature's most negative value.
        dataframe[feature] = dataframe[feature].apply(lambda x: x - feature_min_value)

# Translate the above two negatively-valued features to positive values
translate_negative_valued_features(X_train_raw, non_norm_feat_neg_values_skewed)

# 12. Log-transform all 17 non-normalized numerical features that have skewed distributions. 
# These 17 features include the 2 that were translated to positive ranges in Step 11.

# Add the 2 features translated to positive ranges above in Step 11 to 
# the list of non-normalized skewed features with positive values. This is 
# the set of features that will be log-transformed
log_transform_feats = non_norm_feat_pos_values_skewed + non_norm_feat_neg_values_skewed

X_train_raw[log_transform_feats] = X_train_raw[log_transform_feats].apply(lambda x: np.log(x + 1))

# 13. Replace 'NaN' values for all numerical features with each feature's mean. Fit an imputer 
# to each numerical feature containing at least one 'NaN' entry.

# Create a list of all the 67 numerical features in the main dataframe. These include all 
# 17 features that were log-transformed in Step 12, as well as the 4 normal features that 
# still need to be scaled, as well as the 46 normal features that don't need scaling.
numerical_features = log_transform_feats + norm_feat_need_scaling + norm_feat_not_need_scaling

# Create a list of all numerical features in the training set that have at least one 'NaN' entry
numerical_features_with_nan = X_train_raw[numerical_features].columns[X_train_raw[numerical_features].isna().any()].tolist()

# Create an imputer
imputer = Imputer()
# Fit the imputer to each numerical feature in the training set that has 'NaN' values, 
# and replace each 'NaN' entry of each feature with that feature's mean.
X_train_raw[numerical_features_with_nan] = imputer.fit_transform(X_train_raw[numerical_features_with_nan])

# 14. Remove the borrower ID column, SK_ID_CURR, from the main dataframe
X_train_raw = X_train_raw.drop('SK_ID_CURR', axis=1)

# 15. One-hot encode all 19 non-binary categorical features.
X_train_raw = pd.get_dummies(X_train_raw, columns=cat_feat_need_one_hot)

# Create a list that includes only the newly one-hot encoded features 
# as well as all the categorical features that were already binary.
all_bin_cat_feat = X_train_raw.columns.tolist()
for column_name in X_train_raw[numerical_features].columns.tolist():
    all_bin_cat_feat.remove(column_name)

# 16. Replace all 'NaN' values in all binary categorical features with 0.

# Create a list of binary categorical features with at least one 'NaN' entry
bin_cat_feat_with_nan = X_train_raw[all_bin_cat_feat].columns[X_train_raw[all_bin_cat_feat].isna().any()].tolist()

# Replace each 'NaN' value in each of these binary features with 0
X_train_raw[bin_cat_feat_with_nan] = X_train_raw[bin_cat_feat_with_nan].fillna(value=0)

# 17. Fit a min-max scaler to each of the 17 log-transformed numerical features, as well 
# as to the 4 features DAYS_BIRTH, DAYS_ID_PUBLISH, HOUR_APPR_PROCESS_START, and the normalized 
# feature REGION_POPULATION_RELATIVE. Each feature will be scaled to a range [0.0, 1.0].

# Build a list of all 21 features needing scaling. Add the list of features that 
# were log-normalized above in Step 12 to the list of normally shaped features 
# that need to be scaled to the range [0,1].
feats_to_scale = norm_feat_need_scaling + log_transform_feats

# Initialize a scaler with the default range of [0,1]
scaler = MinMaxScaler()

# Fit the scaler to each of the features of the train set that need to be scaled, 
# then transform each of these features' values to the new scale.
X_train_raw[feats_to_scale] = scaler.fit_transform(X_train_raw[feats_to_scale])

# Rename the dataframe to indicate that its columns have been fully preprocessed.
X_train_processed = X_train_raw
In [617]:
# 18. Fit PCA on all numerical features and observe how many 
# components explain approximately 90% of the variance in the data.
pca = PCA(n_components = 17)
pca.fit(X_train_processed[numerical_features])

# Number of components used for pca
n_components = len(pca.explained_variance_ratio_)

# The total percent explained variance of all components used in PCA
percent_explained_var_all_n_components = round(sum(pca.explained_variance_ratio_)*100, 2)

print('Explained variance ratios for each component:')
print(pca.explained_variance_ratio_)
print('\r')
print('{}% of variance of numerical features explained by {} components.'.format(percent_explained_var_all_n_components, n_components))
Explained variance ratios for each component:
[ 0.1712049   0.12231898  0.09857175  0.06724392  0.05962164  0.05682911
  0.05083881  0.04976384  0.04510004  0.04230571  0.02981382  0.02512118
  0.01950228  0.01827228  0.01581516  0.0155643   0.01255201]

90.04% of variance of numerical features explained by 17 components.
In [618]:
# Display the head of the dataframe created to store the values of the reduced numerical 
# feature dimensions output by PCA.
display(X_train_reduced_numerical_features.head())
PCA Dimension 1 PCA Dimension 2 PCA Dimension 3 PCA Dimension 4 PCA Dimension 5 PCA Dimension 6 PCA Dimension 7 PCA Dimension 8 PCA Dimension 9 PCA Dimension 10 PCA Dimension 11 PCA Dimension 12 PCA Dimension 13 PCA Dimension 14 PCA Dimension 15 PCA Dimension 16 PCA Dimension 17
123473 0.038490 -0.418327 0.132259 0.032978 0.035820 -0.006637 -0.247143 0.114916 0.081367 0.163978 0.134768 0.067258 0.014435 -0.247486 -0.032648 -0.045991 -0.008241
10118 -0.334238 0.278945 0.174923 0.010723 -0.197557 -0.111055 0.053329 -0.051524 -0.099175 0.044238 0.226315 0.100282 0.023050 0.038110 0.020145 0.152308 -0.041128
64716 0.096831 -0.553266 -0.020605 -0.092755 -0.095095 0.246250 0.112898 -0.013167 -0.083315 0.151876 -0.009915 0.117327 0.004433 0.006461 -0.058605 -0.017181 -0.060475
234940 0.139965 0.268606 -0.222063 0.105934 -0.107170 0.132780 -0.109850 -0.154121 0.005824 0.235340 0.152449 0.101618 -0.381045 -0.111174 -0.069584 -0.071027 0.001939
236051 0.016087 -0.177554 0.032054 0.062543 -0.231354 0.287856 0.218970 0.153541 -0.037740 0.055238 -0.078110 -0.295355 0.007658 -0.032789 -0.052711 0.014485 0.023411

5 rows × 17 columns

In [619]:
# 19. Use PCA to reduce the dimension space of the numerical features 
# to the optimal number of principle components discovered above.

reduced_numerical_data = pca.transform(X_train_processed[numerical_features])

# Create a DataFrame for the reduced data
X_train_reduced_numerical_features = pd.DataFrame(reduced_numerical_data, index=X_train_processed.index, columns = [
    'PCA Dimension 1', 'PCA Dimension 2', 'PCA Dimension 3',
    'PCA Dimension 4', 'PCA Dimension 5', 'PCA Dimension 6',
    'PCA Dimension 7', 'PCA Dimension 8', 'PCA Dimension 9',
    'PCA Dimension 10', 'PCA Dimension 11', 'PCA Dimension 12',
    'PCA Dimension 13', 'PCA Dimension 14', 'PCA Dimension 15',
    'PCA Dimension 16', 'PCA Dimension 17'
])

# 20. Drop all 67 numerical features from the original preprocessed 
# dataframe, so that it only contains the 184 binary categorical features. 
# Append the dataframe containing the reduced numerical features back to 
# this original dataframe.

# Drop the 67 original numerical features from the dataframe
X_train_processed = X_train_processed.drop(numerical_features, axis=1)

# Merge the dataframe with the dataframe containing the  17 reduced 
# numerical features
X_train_final = pd.merge(left=X_train_processed, right=X_train_reduced_numerical_features, left_index=True, right_index=True)

# 21. Build a data preprocessing pipeline to used for all testing sets. 
# This pipeline will recreate all features that were engineered in the 
# training set during the original data preprocessing phase. 
# The pipeline will also apply the imputer, min-max, and PCA transforms 
# originally fit on features in the training set to all datapoints in a 
# testing set.

def adjust_columns_application_test_csv_table(testing_dataframe):
    """
    After it is one-hot encoded, application_test.csv data table will have one 
    extra column, 'REGION_RATING_CLIENT_W_CITY_-1', that is not present in the 
    training dataframe. This column will be removed from the testing datatable 
    in this case. Only 1 of the 48,744 rows in application_test.csv will have a 
    value of 1 for this feature following one-hot encoding. I am not worried 
    about this column's elimination from the testing dataframe affecting predictions.
    
    Additionally, unlike the test validation set, which originally comprised 20% of 
    application_train.csv, application_test.csv will be missing the following columns 
    after it is one-hot encoded:
    
    'CODE_GENDER_XNA', 'NAME_INCOME_TYPE_Maternity leave', 'NAME_FAMILY_STATUS_Unknown'
    
    In this case, we need to insert these columns into the testing dataframe, at 
    the exact same indices they are located at in the fully preprocessed training 
    dataframe.Each inserted column will be filled with all zeros. (If each of these 
    binary features are missing from the application_test.csv data table, we can infer 
    that each borrower in thatdata table obviously would have a 0 for each feature were 
    it present.)

    Parameters:
        testing_dataframe: Pandas dataframe containing the testing dataset
                           contained in the file application_test.csv
        
    Returns: a testing dataframe containing the exact same columns and 
             column order as found in the training dataframe
    """
    
    # Identify any columns in the one-hot encoded testing_dataframe that 
    # are not in X_train_raw. These columns will need to be removed from the
    # testing_dataframe. (Expected that there will only be one such 
    # column: 'REGION_RATING_CLIENT_W_CITY_-1')
    X_train_columns_list = X_train_raw.columns.tolist()
    testing_dataframe_columns_list = testing_dataframe.columns.tolist()
    for column_name in X_train_columns_list:
        if column_name in testing_dataframe_columns_list:
            testing_dataframe_columns_list.remove(column_name)
            columns_not_in_X_train_raw = testing_dataframe_columns_list
    
    # Drop any column from the testing_dataframe that is not in the 
    # training dataframe. Expected to only be the one column 'REGION_RATING_CLIENT_W_CITY_-1'
    for column in columns_not_in_X_train_raw:
        testing_dataframe = testing_dataframe.drop(column, axis=1)
    
    # Get the column indices of each of the features 'CODE_GENDER_XNA', 
    #'NAME_INCOME_TYPE_Maternity leave', 'NAME_FAMILY_STATUS_Unknown' from 
    # the raw training dataframe (X_train_raw) prior to having having PCA run on it.
    loc_code_gender_training_frame = X_train_raw.columns.get_loc('CODE_GENDER_XNA')
    loc_name_income_type_maternity_leave_training_frame = X_train_raw.columns.get_loc('NAME_INCOME_TYPE_Maternity leave')
    loc_name_family_status_unknown_training_frame = X_train_raw.columns.get_loc('NAME_FAMILY_STATUS_Unknown')

    # Insert each column into the testing dataframe at the same index it had 
    # in the X_train_raw dataframe before PCA was run. Fill each column with all 0s.
    # Order is important. 'CODE_GENDER_XNA' should be inserted first, followed by 
    # 'NAME_INCOME_TYPE_Maternity leave', and then finally 'NAME_FAMILY_STATUS_Unknown'.
    testing_dataframe.insert(loc=loc_code_gender_training_frame, column='CODE_GENDER_XNA', value=0)
    testing_dataframe.insert(loc=loc_name_income_type_maternity_leave_training_frame, column='NAME_INCOME_TYPE_Maternity leave', value=0)
    testing_dataframe.insert(loc=loc_name_family_status_unknown_training_frame, column='NAME_FAMILY_STATUS_Unknown', value=0)
    return testing_dataframe

def test_set_preprocessing_pipeline(testing_dataframe):
    """
    Recreate all features that were engineered in the training set during 
    the original data preprocessing phase. Missing numerical 'NaN' values 
    will be filled with an imputer. Missing binary categorical feature 'NaN' 
    values will be replaced with 0. The pipeline will also apply 
    the min-max and PCA transforms originally fit on features 
    in the training set to numerical features in the testing set.

    Parameters:
        testing_dataframe: Pandas dataframe containing a testing dataset
        
    Returns: a fully preprocessed testing dataframe
    """
    
    # Create the HAS_CHILDREN feature.
    CNT_CHILDREN_test = testing_dataframe['CNT_CHILDREN']
    HAS_CHILDREN = CNT_CHILDREN_test.map(lambda x: 1 if x > 0 else 0)

    # Append the newly engineered HAS_CHILDREN feature to the main dataframe.
    testing_dataframe = testing_dataframe.assign(HAS_CHILDREN=HAS_CHILDREN.values)
    
    # Drop the CNT_CHILDREN column from the main dataframe
    testing_dataframe = testing_dataframe.drop('CNT_CHILDREN', axis=1)

    # Create the NUMBER_FAMILY_MEMBERS feature. 
    CNT_FAM_MEMBERS_test = testing_dataframe['CNT_FAM_MEMBERS']
    NUMBER_FAMILY_MEMBERS = CNT_FAM_MEMBERS_test.map(lambda x: 'one' if x == 1 else ('two' if x == 2 else 'three_plus'))

    # Append the newly engineered FAMILY_SIZE feature to the main dataframe.
    testing_dataframe = testing_dataframe.assign(NUMBER_FAMILY_MEMBERS=NUMBER_FAMILY_MEMBERS.values)
    
    # Drop the CNT_FAM_MEMBERS feature from the main dataframe
    testing_dataframe = testing_dataframe.drop('CNT_FAM_MEMBERS', axis=1)

    # Build the HAS_CREDIT_BUREAU_LOANS_OVERDUE feature
    testing_dataframe = build_feature_HAS_CREDIT_BUREAU_LOANS_OVERDUE(testing_dataframe)
    
    # Create the HAS_JOB feature
    DAYS_EMPLOYED_test = testing_dataframe['DAYS_EMPLOYED']
    HAS_JOB = DAYS_EMPLOYED_test.map(lambda x: 1 if x <= 0 else 0)

    # Append the newly engineered FAMILY_SIZE feature to the main dataframe.
    testing_dataframe = testing_dataframe.assign(HAS_JOB=HAS_JOB.values)
    
    # Drop the DAYS_EMPLOYED feature from the main dataframe
    testing_dataframe = testing_dataframe.drop('DAYS_EMPLOYED', axis=1)
    
    # Translate the two negatively-valued features DAYS_REGISTRATION, and 
    # DAYS_LAST_PHONE_CHANGE to positive values
    translate_negative_valued_features(testing_dataframe, non_norm_feat_neg_values_skewed)
    
    # Log-transform all 17 non-normalized numerical features that have skewed distributions.
    testing_dataframe[log_transform_feats] = testing_dataframe[log_transform_feats].apply(lambda x: np.log(x + 1))
    
    # Create a list of all numerical features in the testing dataframe that have at least one 'NaN' entry
    numerical_features_with_nan_testing = testing_dataframe[numerical_features].columns[testing_dataframe[numerical_features].isna().any()].tolist()
    
    # Use an imputer to replace 'NaN' values for all numerical features with each feature's mean.
    testing_dataframe[numerical_features_with_nan_testing] = imputer.fit_transform(testing_dataframe[numerical_features_with_nan_testing])
    
    # Remove the borrower ID column, SK_ID_CURR, from the main dataframe
    testing_dataframe = testing_dataframe.drop('SK_ID_CURR', axis=1)
    
    # One-hot encode all 19 non-binary categorical features.
    testing_dataframe = pd.get_dummies(testing_dataframe, columns=cat_feat_need_one_hot)

    # After one-hot encoding, the testing dataframe from application_test.csv will be 
    # missing 2 columns that are in the training dataframe. It will also have an extra 
    # column that was not in the training dataframe, giving it 249 total columns. 
    # If this is the case, we need to modify this testing dataframe so that its columns 
    # and column order is consistent with the training dataframe.
    if testing_dataframe.shape[1] == 249:
        testing_dataframe = adjust_columns_application_test_csv_table(testing_dataframe)
    
    # Create a list of the binary categorical features with at least one 'NaN' entry
    bin_cat_feat_with_nan_testing = testing_dataframe[all_bin_cat_feat].columns[testing_dataframe[all_bin_cat_feat].isna().any()].tolist()
    
    # Replace each 'NaN' value in each of these binary features with 0
    testing_dataframe[bin_cat_feat_with_nan_testing] = testing_dataframe[bin_cat_feat_with_nan_testing].fillna(value=0)
     
    # Transform each of the 21 features that need to be scaled to the range [0,1] using 
    # the min-max scaler fit on the training set.
    testing_dataframe[feats_to_scale] = scaler.transform(testing_dataframe[feats_to_scale])
    
    # Use the PCA algorithm fit on the training set to reduce the dimension space of 
    # the numerical features in the testing set.
    reduced_numerical_data_testing = pca.transform(testing_dataframe[numerical_features])

    # Create a DataFrame for the reduced data
    testing_dataframe_reduced_numerical_features = pd.DataFrame(reduced_numerical_data_testing, index=testing_dataframe.index, columns = [
        'PCA Dimension 1', 'PCA Dimension 2', 'PCA Dimension 3',
        'PCA Dimension 4', 'PCA Dimension 5', 'PCA Dimension 6',
        'PCA Dimension 7', 'PCA Dimension 8', 'PCA Dimension 9',
        'PCA Dimension 10', 'PCA Dimension 11', 'PCA Dimension 12',
        'PCA Dimension 13', 'PCA Dimension 14', 'PCA Dimension 15',
        'PCA Dimension 16', 'PCA Dimension 17'
    ])
    
    # Drop the 67 original numerical features from the dataframe
    testing_dataframe = testing_dataframe.drop(numerical_features, axis=1)

    # Merge the dataframe with the dataframe containing the 17 reduced 
    # numerical features.
    testing_dataframe = pd.merge(left=testing_dataframe, right=testing_dataframe_reduced_numerical_features, left_index=True, right_index=True)
    # Return the fully preprocessed testing dataframe
    return testing_dataframe

# 22. Preprocess the test validation set.
X_test_final = test_set_preprocessing_pipeline(X_test_raw)

# Verify that both the training and test validation dataframes have the expected number of columns after 
# preprocessing its data and transforming their features using the PCA algorithm that was fit on the training 
# data's numerical features. It is expected there are 184 binary categorical features, and 17 reduced numerical 
# features for a total of 201 features.
print('Training set preprocessing complete. The final training dataframe now has {} columns. Expected: 201.'.format(X_train_final.shape[1]))
print('Test validation set preprocessing complete. The final test validation dataframe now has {} columns. Expected: 201.'.format(X_test_final.shape[1]))
Training set preprocessing complete. The final training dataframe now has 201 columns. Expected: 201.
Test validation set preprocessing complete. The final test validation dataframe now has 201 columns. Expected: 201.
In [620]:
# Train the classifiers and compute prediction probabilities:

# 1. Use a Gaussian Naive Bayes classifier to make predictions on 
# the test validation set. Calculate the area under ROC curve score of 
# these predictions.

# Fit a Gaussian Naive Bayes classifier to the training dataframe.
clf_naive_bayes = GaussianNB()
clf_naive_bayes.fit(X_train_final, y_train)

# The Naive Bayes estimates of probability of the positive class (TARGET=1):   
# the probability estimate of each borrower making at least one late loan payment.
naive_bayes_PCA_y_score = clf_naive_bayes.predict_proba(X_test_final)[:, 1]

# The area under the ROC curve between the true target values and the 
# probability estimates of the predicted values.
naive_bayes_PCA_roc_auc_score = roc_auc_scorer(y_test, naive_bayes_PCA_y_score)

# Add the Naive Bayes classifier's scores to the results list.
y_score_list.append(naive_bayes_PCA_y_score)
clf_label_list.append('Naive Bayes PCA')

print('Naive Bayes (PCA) test validation set predictions\' ROC AUC score: {}'.format(naive_bayes_PCA_roc_auc_score))

# 2. Use an AdaBoost classifier to make predictions on the test validation set. 
# Calculate the area under ROC curve score of these predictions.

# Fit the AdaBoost classifier, using the parameter for 'n_estimators' discovered 
# when running GridSearchCV on AdaBoost above for the full featureset.
clf_AdaBoost = AdaBoostClassifier(learning_rate=1.0, n_estimators=1000, random_state=42)
clf_AdaBoost.fit(X_train_final, y_train)

# The AdaBoost classifier's estimates of probability of the positive class (TARGET=1):  
# the probability estimate of each borrower making at least one late loan payment.
adaBoost_PCA_y_score = clf_AdaBoost.predict_proba(X_test_final)[:, 1]

# The area under the ROC curve between the true target values and the 
# probability estimates of the predicted values.
adaBoost_PCA_roc_auc_score = roc_auc_scorer(y_test, adaBoost_PCA_y_score)

# Add the AdaBoost classifier's scores to the results list.
y_score_list.append(adaBoost_PCA_y_score)
clf_label_list.append('AdaBoost PCA')

print('AdaBoost (PCA) test validation set predictions\' ROC AUC score: {}'.format(adaBoost_PCA_roc_auc_score))

# 3. Try using a Logistic Regression classifier to make predictions.

# Fit the classifier to the training data.
clf_logistic_regression = LogisticRegression(penalty='l1', random_state=42, solver='liblinear', max_iter=100)
clf_logistic_regression.fit(X_train_final, y_train)

# The logistical regression classifier's estimates of probability of the positive 
# class (TARGET=1): the probability estimate of each borrower making at least one 
# late loan payment.
logistic_regression_PCA_y_score = clf_logistic_regression.predict_proba(X_test_final)[:, 1]

# The area under the ROC curve between the true target values and the 
# probability estimates of the predicted values.
logistic_regression_PCA_roc_auc_score = roc_auc_scorer(y_test, logistic_regression_PCA_y_score)

# Add the Logistic Regression classifier's scores to the results list.
y_score_list.append(logistic_regression_PCA_y_score)
clf_label_list.append('Logistic Regression PCA')

print('Logistic Regression (PCA) test validation set predictions\' ROC AUC score: {}'.format(logistic_regression_PCA_roc_auc_score))

# 4. Try using a Multi-layer Perceptron classifier to make predictions.

# Fit the classifier to the training data.
clf_mlp = MLPClassifier(
    hidden_layer_sizes=100, activation='identity', solver='adam', alpha=0.001, batch_size=200, 
    learning_rate_init=0.001, random_state=42, warm_start=False
)
clf_mlp.fit(X_train_final, y_train)

# The multi-layer perceptron classifier's estimates of probability of the positive 
# class (TARGET=1): the probability estimate of each borrower making at least one 
# late loan payment.
mlp_PCA_y_score = clf_mlp.predict_proba(X_test_final)[:, 1]

# The area under the ROC curve between the true target values and the 
# probability estimates of the predicted values.
mlp_PCA_roc_auc_score = roc_auc_scorer(y_test, mlp_PCA_y_score)

# Add the Multi-Layer Perceptron classifier's scores to the results list.
y_score_list.append(mlp_PCA_y_score)
clf_label_list.append('Multi-Layer Perceptron PCA')

print('Multi-layer Perceptron (PCA) test validation set predictions\' ROC AUC score: {}'.format(mlp_PCA_roc_auc_score))

# 5. Try using a LightGBM classifier.

# Convert preprocessed training dataset into LightGBM dataset format
lightgbm_training = lgb.Dataset(X_train_final, label=y_train)

# Specify parameters
params = {}
params['learning_rate'] = 0.01
params['boosting_type'] = 'gbdt'
params['objective'] = 'binary'
params['metric'] = 'auc'
params['sub_feature'] = 0.3
params['num_leaves'] = 100
params['min_data_in_leaf'] = 500
params['max_depth'] = 10
params['max_bin'] = 64
#params['min_data_in_bin'] = 3
#params['lambda_l1'] = 0.01
params['lambda_l2'] = 0.01
#params['min_gain_to_split'] = 0.01
params['bagging_freq'] = 100
params['bagging_fraction'] = 0.9
#params['feature_fraction'] = 0.5

# Fit the LightGBM classifier to the training data
clf_lgb = lgb.train(params, lightgbm_training, 1500)

# Classifier's estimates of probability of the positive class (TARGET=1): the 
# probability estimate of each borrower making at least one late loan payment.
lgb_PCA_y_score = clf_lgb.predict(X_test_final)

# The area under the ROC curve between the true target values and the 
# probability estimates of the predicted values.
lgb_PCA_roc_auc_score = roc_auc_scorer(y_test, lgb_PCA_y_score)

# Add the LightGBM classifier's scores to the results list.
y_score_list.append(lgb_PCA_y_score)
clf_label_list.append('LightGBM PCA')

print('LightGBM (PCA) test validation set predictions\' ROC AUC score: {}'.format(lgb_PCA_roc_auc_score))
Naive Bayes (PCA) test validation set predictions' ROC AUC score: 0.5452255614331999
AdaBoost (PCA) test validation set predictions' ROC AUC score: 0.7415669749755673
Logistic Regression (PCA) test validation set predictions' ROC AUC score: 0.743963963781135
Multi-layer Perceptron (PCA) test validation set predictions' ROC AUC score: 0.7439527449175637
LightGBM (PCA) test validation set predictions' ROC AUC score: 0.7483887050110797

SelectKBest Feature Selection

In [633]:
# Step 2. Try training all classifiers on a featureset where SelectKBest feature selection 
# has been used to narrow down the full featureset's 251 features to the best performing features.

# Preprocess the dataset similar to how it was done above. However, this time use SelectKBest 
# to only use a portion of the 251 features that exist after one-hot encoding.

# Load the main data tables
application_train_data = pd.read_csv("data/application_train.csv")
application_test_data = pd.read_csv("data/application_test.csv")

# Load the Bureau data table
bureau_data = pd.read_csv("data/bureau.csv")

# 1: Create lists of different feature types in the main data 
# frame, based on how each type will need to be preprocessed.

# i. All 18 categorical features needing one-hot encoding. 
#    Includes the 4 categorical features originally 
#    mis-identified as having been normalized:
#    EMERGENCYSTATE_MODE, HOUSETYPE_MODE, WALLSMATERIAL_MODE, 
#    FONDKAPREMONT_MODE
cat_feat_need_one_hot = [
    'NAME_CONTRACT_TYPE', 'CODE_GENDER', 'FLAG_OWN_CAR', 
    'FLAG_OWN_REALTY', 'NAME_INCOME_TYPE', 'NAME_EDUCATION_TYPE', 
    'NAME_FAMILY_STATUS', 'NAME_HOUSING_TYPE', 'REGION_RATING_CLIENT',
    'REGION_RATING_CLIENT_W_CITY', 'WEEKDAY_APPR_PROCESS_START', 'ORGANIZATION_TYPE',
    'NAME_TYPE_SUITE', 'OCCUPATION_TYPE', 'EMERGENCYSTATE_MODE',
    'HOUSETYPE_MODE', 'WALLSMATERIAL_MODE', 'FONDKAPREMONT_MODE'
]

# ii. All 32 binary categorical features already one-hot encoded.
bin_cat_feat = [
    'FLAG_MOBIL', 'FLAG_EMP_PHONE', 'FLAG_WORK_PHONE',
    'FLAG_CONT_MOBILE', 'FLAG_PHONE', 'FLAG_EMAIL',
    'REG_REGION_NOT_LIVE_REGION', 'REG_REGION_NOT_WORK_REGION', 'LIVE_REGION_NOT_WORK_REGION',
    'REG_CITY_NOT_LIVE_CITY', 'REG_CITY_NOT_WORK_CITY', 'LIVE_CITY_NOT_WORK_CITY',
    'FLAG_DOCUMENT_2', 'FLAG_DOCUMENT_3', 'FLAG_DOCUMENT_4',
    'FLAG_DOCUMENT_5', 'FLAG_DOCUMENT_6', 'FLAG_DOCUMENT_7',
    'FLAG_DOCUMENT_8', 'FLAG_DOCUMENT_9', 'FLAG_DOCUMENT_10',
    'FLAG_DOCUMENT_11', 'FLAG_DOCUMENT_12', 'FLAG_DOCUMENT_13',
    'FLAG_DOCUMENT_14', 'FLAG_DOCUMENT_15', 'FLAG_DOCUMENT_16',
    'FLAG_DOCUMENT_17', 'FLAG_DOCUMENT_18', 'FLAG_DOCUMENT_19',
    'FLAG_DOCUMENT_20', 'FLAG_DOCUMENT_21'
]

# iii. All 2 non-normalized numerical features with skewed distributions 
#    and negative values. These features will need to have their 
#    distributions translated to positive ranges before being 
#    log-transformed, and then later scaled to the range [0,1].
non_norm_feat_neg_values_skewed = [
    'DAYS_REGISTRATION', 'DAYS_LAST_PHONE_CHANGE'
]


# iv. All 15 non-normalized numerical features with skewed distributions, 
#    and only positive values. These features will need to be 
#    log-transformed, and eventually scaled to the range [0,1].
non_norm_feat_pos_values_skewed = [
    'AMT_INCOME_TOTAL', 'AMT_CREDIT', 'AMT_ANNUITY',
    'AMT_GOODS_PRICE', 'OBS_30_CNT_SOCIAL_CIRCLE', 'DEF_30_CNT_SOCIAL_CIRCLE',
    'OBS_60_CNT_SOCIAL_CIRCLE', 'DEF_60_CNT_SOCIAL_CIRCLE', 'AMT_REQ_CREDIT_BUREAU_HOUR',
    'AMT_REQ_CREDIT_BUREAU_DAY', 'AMT_REQ_CREDIT_BUREAU_WEEK', 'AMT_REQ_CREDIT_BUREAU_MON',
    'AMT_REQ_CREDIT_BUREAU_QRT', 'AMT_REQ_CREDIT_BUREAU_YEAR', 'OWN_CAR_AGE'
]

# v. All 4 numerical features with normal shapes but needing to be scaled 
#    to the range [0,1].
norm_feat_need_scaling = [
    'DAYS_BIRTH', 'DAYS_ID_PUBLISH', 'HOUR_APPR_PROCESS_START',
    'REGION_POPULATION_RELATIVE'
]

# vi. All 46 numerical features that have been normalized to the range 
#   [0,1]. These features will need neither log-transformation, nor 
#   any further scaling.
norm_feat_not_need_scaling = [
    'EXT_SOURCE_2', 'EXT_SOURCE_3', 'YEARS_BEGINEXPLUATATION_AVG',
    'YEARS_BEGINEXPLUATATION_MODE', 'YEARS_BEGINEXPLUATATION_MEDI', 'FLOORSMAX_AVG', 
    'FLOORSMAX_MODE', 'FLOORSMAX_MEDI', 'LIVINGAREA_AVG',
    'LIVINGAREA_MODE', 'LIVINGAREA_MEDI', 'ENTRANCES_AVG',
    'ENTRANCES_MODE', 'ENTRANCES_MEDI', 'APARTMENTS_AVG',
    'APARTMENTS_MODE', 'APARTMENTS_MEDI', 'ELEVATORS_AVG', 
    'ELEVATORS_MODE', 'ELEVATORS_MEDI', 'NONLIVINGAREA_AVG',
    'NONLIVINGAREA_MODE', 'NONLIVINGAREA_MEDI', 'EXT_SOURCE_1',
    'BASEMENTAREA_AVG', 'BASEMENTAREA_MODE', 'BASEMENTAREA_MEDI',
    'LANDAREA_AVG', 'LANDAREA_MODE', 'LANDAREA_MEDI',
    'YEARS_BUILD_AVG', 'YEARS_BUILD_MODE', 'YEARS_BUILD_MEDI',
    'FLOORSMIN_AVG', 'FLOORSMIN_MODE', 'FLOORSMIN_MEDI',
    'LIVINGAPARTMENTS_AVG', 'LIVINGAPARTMENTS_MODE', 'LIVINGAPARTMENTS_MEDI',
    'NONLIVINGAPARTMENTS_AVG', 'NONLIVINGAPARTMENTS_MODE', 'NONLIVINGAPARTMENTS_MEDI',
    'COMMONAREA_AVG', 'COMMONAREA_MODE', 'COMMONAREA_MEDI',
    'TOTALAREA_MODE'
]

# vii. The remaining 3 features in the main data frame that will be 
#    re-engineered and transformed into different features
feat_to_be_reengineered = [
    'CNT_CHILDREN', 'CNT_FAM_MEMBERS', 'DAYS_EMPLOYED'
]

# 2: Separate target data from training dataset.
targets = application_train_data['TARGET']
features_raw = application_train_data.drop('TARGET', axis = 1)

# 3: Use train_test_split from sklearn.cross_validation to 
# create a test validation set that is 20% of the size of the total training set: 
# Will allow me to compare performance of various learning algorithms without 
# overfitting to the training data.
X_train_raw, X_test_raw, y_train, y_test = train_test_split(features_raw, 
                                                    targets, 
                                                    test_size = 0.2, 
                                                    random_state = 42)

# 4: Use the CNT_CHILDREN feature to engineer a binary 
# categorical feature called HAS_CHILDREN. If value of CNT_CHILDREN is 
# greater than 0, the value of HAS_CHILDREN will be 1. If value of CNT_CHILDREN is 
# 0, value of HAS_CHILDREN will be 0. 
CNT_CHILDREN_train = X_train_raw['CNT_CHILDREN']
HAS_CHILDREN = CNT_CHILDREN_train.map(lambda x: 1 if x > 0 else 0)

# Append the newly engineered HAS_CHILDREN feature to the main dataframe.
X_train_raw = X_train_raw.assign(HAS_CHILDREN=HAS_CHILDREN.values)

# 5: Drop the CNT_CHILDREN column from the main dataframe
X_train_raw = X_train_raw.drop('CNT_CHILDREN', axis=1)

# Add the new HAS_CHILDREN feature to the list of binary categorical 
# features that are already one-hot encoded. There are now 33 such features.
bin_cat_feat = bin_cat_feat + ['HAS_CHILDREN']

# 6. Use the CNT_FAM_MEMBERS feature to engineer a categorical feature called NUMBER_FAMILY_MEMBERS. 
# If CNT_FAM_MEMBERS is 1.0, then the value of NUMBER_FAMILY_MEMBERS will be 'one'. If CNT_FAM_MEMBERS is 2.0, 
# then NUMBER_FAMILY_MEMBERS will be 'two'. If CNT_FAM_MEMBERS is 3.0 or greater, then NUMBER_FAMILY_MEMBERS will 
# be 'three_plus'.
CNT_FAM_MEMBERS_train = X_train_raw['CNT_FAM_MEMBERS']
NUMBER_FAMILY_MEMBERS = CNT_FAM_MEMBERS_train.map(lambda x: 'one' if x == 1 else ('two' if x == 2 else 'three_plus'))

# Append the newly engineered FAMILY_SIZE feature to the main dataframe.
X_train_raw = X_train_raw.assign(NUMBER_FAMILY_MEMBERS=NUMBER_FAMILY_MEMBERS.values)

# 7. Drop the CNT_FAM_MEMBERS feature from the main dataframe
X_train_raw = X_train_raw.drop('CNT_FAM_MEMBERS', axis=1)

# Add the new NUMBER_FAMILY_MEMBERS feature to the list of categorical 
# features that will need to be one-hot encoded. There are now 19 of these features.
cat_feat_need_one_hot = cat_feat_need_one_hot + ['NUMBER_FAMILY_MEMBERS']

# 8. Use the CREDIT_DAY_OVERDUE feature in bureau.csv to engineer the binary 
# categorical HAS_CREDIT_BUREAU_LOANS_OVERDUE feature. If CREDIT_DAY_OVERDUE for a 
# particular borrower ID (SK_ID_CURR) is greater than 0, then the value of 
# HAS_CREDIT_BUREAU_LOANS_OVERDUE will be 1. If CREDIT_DAY_OVERDUE for a particular 
# borrower ID is 0, then the value of HAS_CREDIT_BUREAU_LOANS_OVERDUE will be 0.

# Filter the bureau data table for loans which are overdue (have a value 
# for CREDIT_DAY_OVERDUE that's greater than 0)
bureau_data_filtered_for_overdue = bureau_data[bureau_data['CREDIT_DAY_OVERDUE'] > 0]

def build_feature_HAS_CREDIT_BUREAU_LOANS_OVERDUE(dataframe):
    """
    Use the CREDIT_DAY_OVERDUE feature in bureau.csv to engineer the binary 
    categorical HAS_CREDIT_BUREAU_LOANS_OVERDUE feature. If CREDIT_DAY_OVERDUE for a 
    particular borrower ID (SK_ID_CURR) is greater than 0, then the value of 
    HAS_CREDIT_BUREAU_LOANS_OVERDUE will be 1. If CREDIT_DAY_OVERDUE for a particular 
    borrower ID is 0, then the value of HAS_CREDIT_BUREAU_LOANS_OVERDUE will be 0.

    Parameters:
        dataframe: Pandas dataframe containing a training or testing dataset
        
    Returns: The dataframe with HAS_CREDIT_BUREAU_LOANS_OVERDUE feature appended to it.
    """
    # Create a series called HAS_CREDIT_BUREAU_LOANS_OVERDUE and fill it with zeros.  
    # Its index is identical to that of the main dataframe. It will eventually be appended 
    # to the main data frame as a column.
    HAS_CREDIT_BUREAU_LOANS_OVERDUE = pd.Series(data=0, index = dataframe['SK_ID_CURR'].index)

    # A list of all the borrowers IDs in the main dataframe
    main_data_table_borrower_IDs = dataframe['SK_ID_CURR'].values

    # For each loan in the bureau data table that is overdue 
    # (has a value for CREDIT_DAY_OVERDUE that's greater than 0)
    for index, row in bureau_data_filtered_for_overdue.iterrows():
        # The borrower ID (SK_ID_CURR) that owns the overdue loan
        borrower_ID = row['SK_ID_CURR']
        # If the borrower ID owning the overdue loan is also 
        # in the main data frame, then enter a value of 1 in 
        # the series HAS_CREDIT_BUREAU_LOANS_OVERDUE at an index 
        # that is identical to the index of the borrower ID 
        # in the main data frame.
        if borrower_ID in main_data_table_borrower_IDs:
            # The index of the borrower's row in the main data table.
            borrower_index_main_data_table = dataframe.index[dataframe['SK_ID_CURR'] == borrower_ID].tolist()[0]
            # Place a value of 1 at the index of the series HAS_CREDIT_BUREAU_LOANS_OVERDUE 
            # which corresponds to the index of the borrower's ID in the main data table.
            HAS_CREDIT_BUREAU_LOANS_OVERDUE.loc[borrower_index_main_data_table] = 1
    # Append the newly engineered HAS_CREDIT_BUREAU_LOANS_OVERDUE feature to the main dataframe.
    dataframe = dataframe.assign(HAS_CREDIT_BUREAU_LOANS_OVERDUE=HAS_CREDIT_BUREAU_LOANS_OVERDUE.values)
    return dataframe

# Build the HAS_CREDIT_BUREAU_LOANS_OVERDUE feature
X_train_raw = build_feature_HAS_CREDIT_BUREAU_LOANS_OVERDUE(X_train_raw)

# Add the new HAS_CREDIT_BUREAU_LOANS_OVERDUE feature to the list of binary categorical 
# features. There are now 34 of these features.
bin_cat_feat = bin_cat_feat + ['HAS_CREDIT_BUREAU_LOANS_OVERDUE']

# 9. Use the DAYS_EMPLOYED feature to engineer a binary categorical feature called HAS_JOB. 
# If the value of DAYS_EMPLOYED is 0 or less, then HAS_JOB will be 1. Otherwise, HAS_JOB will 
# be 0. This condition will apply to all borrowers who had a value of 365243 for DAYS_EMPLOYED, 
# which I hypothesized can be best interpreted as meaning that a borrower does not have a job.
DAYS_EMPLOYED_train = X_train_raw['DAYS_EMPLOYED']
HAS_JOB = DAYS_EMPLOYED_train.map(lambda x: 1 if x <= 0 else 0)

# Append the newly engineered FAMILY_SIZE feature to the main dataframe.
X_train_raw = X_train_raw.assign(HAS_JOB=HAS_JOB.values)

# 10. Drop the DAYS_EMPLOYED feature from the main dataframe
X_train_raw = X_train_raw.drop('DAYS_EMPLOYED', axis=1)

# Add the new HAS_JOB feature to the list of binary categorical features. 
# There are now 35 of these features.
bin_cat_feat = bin_cat_feat + ['HAS_JOB']

# 11. Translate the 2 non-normalized numerical features that have skewed distributions 
# and negative values: DAYS_REGISTRATION, and DAYS_LAST_PHONE_CHANGE

def translate_negative_valued_features(dataframe, feature_name_list):
    """
    Translate a dataset's continuous features containing several negative 
    values. The dataframe is modified such that all values of each feature 
    listed in the feature_name_list parameter become positive.

    Parameters:
        dataframe: Pandas dataframe containing the features
        feature_name_list: List of strings, containing the names 
                           of each feature whose values will be 
                           translated
    """
    for feature in feature_name_list:
        # The minimum, most-negative, value of the feature
        feature_min_value = dataframe[feature].min()
        # Translate each value of the feature in a positive direction, 
        # of magnitude that's equal to the feature's most negative value.
        dataframe[feature] = dataframe[feature].apply(lambda x: x - feature_min_value)

# Translate the above two negatively-valued features to positive values
translate_negative_valued_features(X_train_raw, non_norm_feat_neg_values_skewed)

# 12. Log-transform all 17 non-normalized numerical features that have skewed distributions. 
# These 17 features include the 2 that were translated to positive ranges in Step 11.

# Add the 2 features translated to positive ranges above in Step 11 to 
# the list of non-normalized skewed features with positive values. This is 
# the set of features that will be log-transformed
log_transform_feats = non_norm_feat_pos_values_skewed + non_norm_feat_neg_values_skewed

X_train_raw[log_transform_feats] = X_train_raw[log_transform_feats].apply(lambda x: np.log(x + 1))

# 13. Replace 'NaN' values for all numerical features with each feature's mean. Fit an imputer 
# to each numerical feature containing at least one 'NaN' entry.

# Create a list of all the 67 numerical features in the main dataframe. These include all 
# 17 features that were log-transformed in Step 12, as well as the 4 normal features that 
# still need to be scaled, as well as the 46 normal features that don't need scaling.
numerical_features = log_transform_feats + norm_feat_need_scaling + norm_feat_not_need_scaling

# Create a list of all numerical features in the training set that have at least one 'NaN' entry
numerical_features_with_nan = X_train_raw[numerical_features].columns[X_train_raw[numerical_features].isna().any()].tolist()

# Create an imputer
imputer = Imputer()
# Fit the imputer to each numerical feature in the training set that has 'NaN' values, 
# and replace each 'NaN' entry of each feature with that feature's mean.
X_train_raw[numerical_features_with_nan] = imputer.fit_transform(X_train_raw[numerical_features_with_nan])

# 14. Remove the borrower ID column, SK_ID_CURR, from the main dataframe
X_train_raw = X_train_raw.drop('SK_ID_CURR', axis=1)

# 15. One-hot encode all 19 non-binary categorical features.
X_train_raw = pd.get_dummies(X_train_raw, columns=cat_feat_need_one_hot)

# Create a list that includes only the newly one-hot encoded features 
# as well as all the categorical features that were already binary.
all_bin_cat_feat = X_train_raw.columns.tolist()
for column_name in X_train_raw[numerical_features].columns.tolist():
    all_bin_cat_feat.remove(column_name)

# 16. Replace all 'NaN' values in all binary categorical features with 0.

# Create a list of binary categorical features with at least one 'NaN' entry
bin_cat_feat_with_nan = X_train_raw[all_bin_cat_feat].columns[X_train_raw[all_bin_cat_feat].isna().any()].tolist()

# Replace each 'NaN' value in each of these binary features with 0
X_train_raw[bin_cat_feat_with_nan] = X_train_raw[bin_cat_feat_with_nan].fillna(value=0)

# 17. Fit a min-max scaler to each of the 17 log-transformed numerical features, as well 
# as to the 4 features DAYS_BIRTH, DAYS_ID_PUBLISH, HOUR_APPR_PROCESS_START, and the normalized 
# feature REGION_POPULATION_RELATIVE. Each feature will be scaled to a range [0.0, 1.0].

# Build a list of all 21 features needing scaling. Add the list of features that 
# were log-normalized above in Step 12 to the list of normally shaped features 
# that need to be scaled to the range [0,1].
feats_to_scale = norm_feat_need_scaling + log_transform_feats

# Initialize a scaler with the default range of [0,1]
scaler = MinMaxScaler()

# Fit the scaler to each of the features of the train set that need to be scaled, 
# then transform each of these features' values to the new scale.
X_train_raw[feats_to_scale] = scaler.fit_transform(X_train_raw[feats_to_scale])

# Rename the dataframe to indicate that its columns have been fully preprocessed.
X_train_processed = X_train_raw
In [634]:
# 18. Fit selectKBest to the fully processed full feature set.

selectK = SelectKBest(score_func=f_classif, k=10)
selectK.fit(X_train_processed, y_train)

# Rank each feature by its score in SelectKBest and display
feature_list = X_train_processed.columns.values
feature_importance_list = selectK.scores_
rows_list = []
for i in range(len(feature_importance_list)):
    dictionary = {}
    dictionary['Feature Name'] = feature_list[i]
    dictionary['Score'] = feature_importance_list[i]
    rows_list.append(dictionary)
    
selectKBest_feature_scores = pd.DataFrame(rows_list, columns=['Feature Name', 'Score'])
selectKBest_feature_scores_ranked = selectKBest_feature_scores.sort_values('Score',ascending=False)

# Ranked scores of each feature using SelectKBest with a 
# f_classif scorer.
display(selectKBest_feature_scores_ranked)
Feature Name Score
23 EXT_SOURCE_2 6351.644583
24 EXT_SOURCE_3 6328.437711
22 EXT_SOURCE_1 2418.609627
5 DAYS_BIRTH 1491.612647
120 NAME_EDUCATION_TYPE_Higher education 770.750915
118 NAME_INCOME_TYPE_Working 762.909544
105 CODE_GENDER_M 741.230537
104 CODE_GENDER_F 741.011078
7 DAYS_ID_PUBLISH 672.617697
72 DAYS_LAST_PHONE_CHANGE 664.547926
20 REG_CITY_NOT_WORK_CITY 641.963446
141 REGION_RATING_CLIENT_W_CITY_3 617.904943
123 NAME_EDUCATION_TYPE_Secondary / secondary special 587.421576
138 REGION_RATING_CLIENT_3 559.755872
114 NAME_INCOME_TYPE_Pensioner 519.156107
101 HAS_JOB 514.506126
206 ORGANIZATION_TYPE_XNA 514.506126
10 FLAG_EMP_PHONE 514.247189
74 FLAG_DOCUMENT_3 489.176233
19 REG_CITY_NOT_LIVE_CITY 487.748885
222 OCCUPATION_TYPE_Laborers 451.649992
232 EMERGENCYSTATE_MODE_No 430.986786
6 DAYS_REGISTRATION 426.892776
139 REGION_RATING_CLIENT_W_CITY_1 419.102104
136 REGION_RATING_CLIENT_1 391.975347
234 HOUSETYPE_MODE_block of flats 391.084471
4 REGION_POPULATION_RELATIVE 335.592000
69 DEF_30_CNT_SOCIAL_CIRCLE 276.154409
21 LIVE_CITY_NOT_WORK_CITY 271.268415
71 DEF_60_CNT_SOCIAL_CIRCLE 262.072203
241 WALLSMATERIAL_MODE_Panel 261.178193
103 NAME_CONTRACT_TYPE_Revolving loans 247.628491
102 NAME_CONTRACT_TYPE_Cash loans 247.628491
32 FLOORSMAX_AVG 211.969217
135 NAME_HOUSING_TYPE_With parents 210.523971
191 ORGANIZATION_TYPE_Self-employed 209.063468
11 FLAG_WORK_PHONE 208.123695
60 FLOORSMAX_MEDI 207.749142
46 FLOORSMAX_MODE 203.088318
218 OCCUPATION_TYPE_Drivers 200.625618
77 FLAG_DOCUMENT_6 197.942808
100 HAS_CREDIT_BUREAU_LOANS_OVERDUE 197.189819
223 OCCUPATION_TYPE_Low-skill Laborers 186.988198
131 NAME_HOUSING_TYPE_House / apartment 180.158575
127 NAME_FAMILY_STATUS_Single / not married 170.537185
125 NAME_FAMILY_STATUS_Married 151.100606
154 ORGANIZATION_TYPE_Business Entity Type 3 149.376673
8 OWN_CAR_AGE 147.047893
13 FLAG_PHONE 136.936022
3 AMT_GOODS_PRICE 132.906700
108 FLAG_OWN_CAR_Y 132.261176
107 FLAG_OWN_CAR_N 132.261176
115 NAME_INCOME_TYPE_State servant 127.037951
15 HOUR_APPR_PROCESS_START 126.874585
246 FONDKAPREMONT_MODE_reg oper account 125.770412
124 NAME_FAMILY_STATUS_Civil marriage 124.294047
36 LIVINGAREA_AVG 119.925762
64 LIVINGAREA_MEDI 119.437499
67 TOTALAREA_MODE 119.138985
30 ELEVATORS_AVG 117.493051
58 ELEVATORS_MEDI 114.950479
214 OCCUPATION_TYPE_Accountants 110.879705
99 HAS_CHILDREN 106.044789
50 LIVINGAREA_MODE 105.500373
44 ELEVATORS_MODE 102.355106
156 ORGANIZATION_TYPE_Construction 99.328685
129 NAME_FAMILY_STATUS_Widow 98.631146
217 OCCUPATION_TYPE_Core staff 91.826127
228 OCCUPATION_TYPE_Sales staff 91.511697
25 APARTMENTS_AVG 88.609484
53 APARTMENTS_MEDI 87.332153
134 NAME_HOUSING_TYPE_Rented apartment 85.727777
250 NUMBER_FAMILY_MEMBERS_two 83.000603
0 AMT_INCOME_TOTAL 79.697778
224 OCCUPATION_TYPE_Managers 76.848477
39 APARTMENTS_MODE 76.238322
33 FLOORSMIN_AVG 75.487045
203 ORGANIZATION_TYPE_Transport: type 3 73.694931
61 FLOORSMIN_MEDI 73.091264
98 AMT_REQ_CREDIT_BUREAU_YEAR 70.392198
47 FLOORSMIN_MODE 70.376105
249 NUMBER_FAMILY_MEMBERS_three_plus 63.195011
220 OCCUPATION_TYPE_High skill tech staff 50.862403
230 OCCUPATION_TYPE_Security staff 50.014630
26 BASEMENTAREA_AVG 48.016617
188 ORGANIZATION_TYPE_School 46.025391
54 BASEMENTAREA_MEDI 45.458173
242 WALLSMATERIAL_MODE_Stone, brick 44.926757
1 AMT_CREDIT 41.884275
216 OCCUPATION_TYPE_Cooking staff 40.940439
31 ENTRANCES_AVG 39.429477
59 ENTRANCES_MEDI 39.400943
35 LIVINGAPARTMENTS_AVG 37.988358
63 LIVINGAPARTMENTS_MEDI 36.611648
87 FLAG_DOCUMENT_16 36.435649
40 BASEMENTAREA_MODE 35.665519
56 YEARS_BUILD_MEDI 35.358525
28 YEARS_BUILD_AVG 35.246605
122 NAME_EDUCATION_TYPE_Lower secondary 34.820010
42 YEARS_BUILD_MODE 34.147157
137 REGION_RATING_CLIENT_2 33.593134
187 ORGANIZATION_TYPE_Restaurant 33.449146
49 LIVINGAPARTMENTS_MODE 33.001197
45 ENTRANCES_MODE 32.858163
247 FONDKAPREMONT_MODE_reg oper spec account 30.999298
84 FLAG_DOCUMENT_13 30.248832
245 FONDKAPREMONT_MODE_org spec account 29.872789
57 COMMONAREA_MEDI 27.419156
29 COMMONAREA_AVG 27.195589
140 REGION_RATING_CLIENT_W_CITY_2 26.920902
112 NAME_INCOME_TYPE_Commercial associate 26.407128
179 ORGANIZATION_TYPE_Medicine 25.878244
96 AMT_REQ_CREDIT_BUREAU_MON 24.883604
180 ORGANIZATION_TYPE_Military 24.860737
160 ORGANIZATION_TYPE_Government 23.012125
183 ORGANIZATION_TYPE_Police 22.996228
190 ORGANIZATION_TYPE_Security Ministries 22.325024
85 FLAG_DOCUMENT_14 22.270497
151 ORGANIZATION_TYPE_Bank 21.582545
68 OBS_30_CNT_SOCIAL_CIRCLE 21.539336
70 OBS_60_CNT_SOCIAL_CIRCLE 20.763439
239 WALLSMATERIAL_MODE_Monolithic 20.625953
243 WALLSMATERIAL_MODE_Wooden 20.565558
43 COMMONAREA_MODE 20.548122
169 ORGANIZATION_TYPE_Industry: type 3 20.308288
73 FLAG_DOCUMENT_2 18.931986
225 OCCUPATION_TYPE_Medicine staff 17.111182
89 FLAG_DOCUMENT_18 16.691907
38 NONLIVINGAREA_AVG 15.939986
200 ORGANIZATION_TYPE_Trade: type 7 15.807700
66 NONLIVINGAREA_MEDI 15.601615
196 ORGANIZATION_TYPE_Trade: type 3 15.230959
150 ORGANIZATION_TYPE_Agriculture 15.174833
163 ORGANIZATION_TYPE_Industry: type 1 15.080143
231 OCCUPATION_TYPE_Waiters/barmen staff 15.003952
55 YEARS_BEGINEXPLUATATION_MEDI 14.919927
205 ORGANIZATION_TYPE_University 14.719397
52 NONLIVINGAREA_MODE 14.526821
79 FLAG_DOCUMENT_8 14.285048
117 NAME_INCOME_TYPE_Unemployed 14.129753
27 YEARS_BEGINEXPLUATATION_AVG 13.881824
215 OCCUPATION_TYPE_Cleaning staff 12.054264
109 FLAG_OWN_REALTY_N 11.936645
110 FLAG_OWN_REALTY_Y 11.936645
208 NAME_TYPE_SUITE_Family 11.908629
41 YEARS_BEGINEXPLUATATION_MODE 11.846402
213 NAME_TYPE_SUITE_Unaccompanied 11.159846
237 WALLSMATERIAL_MODE_Block 10.779570
166 ORGANIZATION_TYPE_Industry: type 12 10.587189
189 ORGANIZATION_TYPE_Security 10.553042
204 ORGANIZATION_TYPE_Transport: type 4 10.501781
233 EMERGENCYSTATE_MODE_Yes 10.331828
17 REG_REGION_NOT_WORK_REGION 9.773022
62 LANDAREA_MEDI 9.456271
177 ORGANIZATION_TYPE_Kindergarten 8.910753
16 REG_REGION_NOT_LIVE_REGION 8.888041
34 LANDAREA_AVG 8.487783
86 FLAG_DOCUMENT_15 8.097889
48 LANDAREA_MODE 7.846998
199 ORGANIZATION_TYPE_Trade: type 6 7.657575
97 AMT_REQ_CREDIT_BUREAU_QRT 6.639310
248 NUMBER_FAMILY_MEMBERS_one 6.402200
133 NAME_HOUSING_TYPE_Office apartment 5.850871
119 NAME_EDUCATION_TYPE_Academic degree 5.834419
235 HOUSETYPE_MODE_specific housing 5.799445
182 ORGANIZATION_TYPE_Other 5.775608
170 ORGANIZATION_TYPE_Industry: type 4 5.669210
175 ORGANIZATION_TYPE_Industry: type 9 5.574003
211 NAME_TYPE_SUITE_Other_B 5.037998
94 AMT_REQ_CREDIT_BUREAU_DAY 4.873239
113 NAME_INCOME_TYPE_Maternity leave 4.732608
153 ORGANIZATION_TYPE_Business Entity Type 2 4.640050
143 WEEKDAY_APPR_PROCESS_START_MONDAY 4.344612
158 ORGANIZATION_TYPE_Electricity 4.222862
147 WEEKDAY_APPR_PROCESS_START_TUESDAY 4.214625
195 ORGANIZATION_TYPE_Trade: type 2 3.997287
226 OCCUPATION_TYPE_Private service staff 3.974990
229 OCCUPATION_TYPE_Secretaries 3.938090
82 FLAG_DOCUMENT_11 3.862618
157 ORGANIZATION_TYPE_Culture 3.822379
244 FONDKAPREMONT_MODE_not specified 3.709716
185 ORGANIZATION_TYPE_Realtor 3.672893
80 FLAG_DOCUMENT_9 3.538684
161 ORGANIZATION_TYPE_Hotel 3.293603
121 NAME_EDUCATION_TYPE_Incomplete higher 3.078681
144 WEEKDAY_APPR_PROCESS_START_SATURDAY 2.607667
219 OCCUPATION_TYPE_HR staff 2.507271
197 ORGANIZATION_TYPE_Trade: type 4 2.406371
181 ORGANIZATION_TYPE_Mobile 2.100879
184 ORGANIZATION_TYPE_Postal 2.033214
155 ORGANIZATION_TYPE_Cleaning 1.943421
88 FLAG_DOCUMENT_17 1.820263
198 ORGANIZATION_TYPE_Trade: type 5 1.756634
159 ORGANIZATION_TYPE_Emergency 1.615530
75 FLAG_DOCUMENT_4 1.582233
201 ORGANIZATION_TYPE_Transport: type 1 1.524013
192 ORGANIZATION_TYPE_Services 1.479096
92 FLAG_DOCUMENT_21 1.449279
165 ORGANIZATION_TYPE_Industry: type 11 1.438828
171 ORGANIZATION_TYPE_Industry: type 5 1.425255
162 ORGANIZATION_TYPE_Housing 1.415296
18 LIVE_REGION_NOT_WORK_REGION 1.376643
194 ORGANIZATION_TYPE_Trade: type 1 1.342503
210 NAME_TYPE_SUITE_Other_A 1.334424
116 NAME_INCOME_TYPE_Student 1.318510
142 WEEKDAY_APPR_PROCESS_START_FRIDAY 1.272125
202 ORGANIZATION_TYPE_Transport: type 2 1.205310
164 ORGANIZATION_TYPE_Industry: type 10 1.188234
176 ORGANIZATION_TYPE_Insurance 1.155082
236 HOUSETYPE_MODE_terraced house 1.085247
132 NAME_HOUSING_TYPE_Municipal apartment 1.038929
78 FLAG_DOCUMENT_7 0.989680
145 WEEKDAY_APPR_PROCESS_START_SUNDAY 0.919733
240 WALLSMATERIAL_MODE_Others 0.869154
111 NAME_INCOME_TYPE_Businessman 0.791085
37 NONLIVINGAPARTMENTS_AVG 0.563170
126 NAME_FAMILY_STATUS_Separated 0.526550
207 NAME_TYPE_SUITE_Children 0.494781
212 NAME_TYPE_SUITE_Spouse, partner 0.441211
65 NONLIVINGAPARTMENTS_MEDI 0.427392
238 WALLSMATERIAL_MODE_Mixed 0.417794
2 AMT_ANNUITY 0.413282
93 AMT_REQ_CREDIT_BUREAU_HOUR 0.394736
193 ORGANIZATION_TYPE_Telecom 0.385876
178 ORGANIZATION_TYPE_Legal Services 0.384521
91 FLAG_DOCUMENT_20 0.380741
81 FLAG_DOCUMENT_10 0.351586
221 OCCUPATION_TYPE_IT staff 0.337380
227 OCCUPATION_TYPE_Realty agents 0.335111
76 FLAG_DOCUMENT_5 0.310443
209 NAME_TYPE_SUITE_Group of people 0.303351
106 CODE_GENDER_XNA 0.263688
148 WEEKDAY_APPR_PROCESS_START_WEDNESDAY 0.228726
51 NONLIVINGAPARTMENTS_MODE 0.152578
152 ORGANIZATION_TYPE_Business Entity Type 1 0.101873
146 WEEKDAY_APPR_PROCESS_START_THURSDAY 0.097273
128 NAME_FAMILY_STATUS_Unknown 0.087895
83 FLAG_DOCUMENT_12 0.087895
9 FLAG_MOBIL 0.087893
172 ORGANIZATION_TYPE_Industry: type 6 0.080245
173 ORGANIZATION_TYPE_Industry: type 7 0.064579
12 FLAG_CONT_MOBILE 0.064415
167 ORGANIZATION_TYPE_Industry: type 13 0.054384
174 ORGANIZATION_TYPE_Industry: type 8 0.030311
90 FLAG_DOCUMENT_19 0.028870
149 ORGANIZATION_TYPE_Advertising 0.014839
130 NAME_HOUSING_TYPE_Co-op apartment 0.007719
186 ORGANIZATION_TYPE_Religion 0.006141
95 AMT_REQ_CREDIT_BUREAU_WEEK 0.002720
168 ORGANIZATION_TYPE_Industry: type 2 0.000671
14 FLAG_EMAIL 0.000029

251 rows × 2 columns

In [635]:
# Select the top k=30 features according to their f_classif scores.
selectK_top_features = selectKBest_feature_scores_ranked['Feature Name'].values[:30]
print(selectK_top_features)
['EXT_SOURCE_2' 'EXT_SOURCE_3' 'EXT_SOURCE_1' 'DAYS_BIRTH'
 'NAME_EDUCATION_TYPE_Higher education' 'NAME_INCOME_TYPE_Working'
 'CODE_GENDER_M' 'CODE_GENDER_F' 'DAYS_ID_PUBLISH' 'DAYS_LAST_PHONE_CHANGE'
 'REG_CITY_NOT_WORK_CITY' 'REGION_RATING_CLIENT_W_CITY_3'
 'NAME_EDUCATION_TYPE_Secondary / secondary special'
 'REGION_RATING_CLIENT_3' 'NAME_INCOME_TYPE_Pensioner' 'HAS_JOB'
 'ORGANIZATION_TYPE_XNA' 'FLAG_EMP_PHONE' 'FLAG_DOCUMENT_3'
 'REG_CITY_NOT_LIVE_CITY' 'OCCUPATION_TYPE_Laborers'
 'EMERGENCYSTATE_MODE_No' 'DAYS_REGISTRATION'
 'REGION_RATING_CLIENT_W_CITY_1' 'REGION_RATING_CLIENT_1'
 'HOUSETYPE_MODE_block of flats' 'REGION_POPULATION_RELATIVE'
 'DEF_30_CNT_SOCIAL_CIRCLE' 'LIVE_CITY_NOT_WORK_CITY'
 'DEF_60_CNT_SOCIAL_CIRCLE']
In [636]:
# Determine what number of features have an aggregate f_classif score that 
# comprises 90% of the aggregate f_classif of all features.

aggregate_f_classif_score_all_features = selectKBest_feature_scores_ranked['Score'].sum()
aggregate_f_classif_score_top_30_features = selectKBest_feature_scores_ranked[:30]['Score'].sum()
print('Aggregate f_classif score of all 251 features: {}'.format(aggregate_f_classif_score_all_features))
print('Aggregate f_classif score of top 30 features: {}'.format(aggregate_f_classif_score_all_features))
print('Top 30 features\' total score is {}% of the total score of all 251 features.'.format(round(aggregate_f_classif_score_top_30_features*100./aggregate_f_classif_score_all_features,2)))
Aggregate f_classif score of all 251 features: 39060.04565296721
Aggregate f_classif score of top 30 features: 39060.04565296721
Top 30 features' total score is 76.92% of the total score of all 251 features.
In [637]:
# Reduce the training dataset to the top 30 features:
X_train_final = X_train_processed[selectK_top_features]
In [638]:
display(X_train_final)
EXT_SOURCE_2 EXT_SOURCE_3 EXT_SOURCE_1 DAYS_BIRTH NAME_EDUCATION_TYPE_Higher education NAME_INCOME_TYPE_Working CODE_GENDER_M CODE_GENDER_F DAYS_ID_PUBLISH DAYS_LAST_PHONE_CHANGE REG_CITY_NOT_WORK_CITY REGION_RATING_CLIENT_W_CITY_3 NAME_EDUCATION_TYPE_Secondary / secondary special REGION_RATING_CLIENT_3 NAME_INCOME_TYPE_Pensioner HAS_JOB ORGANIZATION_TYPE_XNA FLAG_EMP_PHONE FLAG_DOCUMENT_3 REG_CITY_NOT_LIVE_CITY OCCUPATION_TYPE_Laborers EMERGENCYSTATE_MODE_No DAYS_REGISTRATION REGION_RATING_CLIENT_W_CITY_1 REGION_RATING_CLIENT_1 HOUSETYPE_MODE_block of flats REGION_POPULATION_RELATIVE DEF_30_CNT_SOCIAL_CIRCLE LIVE_CITY_NOT_WORK_CITY DEF_60_CNT_SOCIAL_CIRCLE
123473 0.358568 0.563835 0.524685 0.105975 0 0 0 1 0.387523 0.921965 0 0 1 0 1 0 1 0 1 0 0 0 0.983434 0 0 0 0.491595 0.000000 0 0.000000
10118 0.490305 0.595456 0.244926 0.806539 0 1 1 0 0.527720 0.997950 0 0 1 0 0 1 0 1 1 0 1 1 0.979934 0 0 1 0.134897 0.000000 0 0.000000
64716 0.643404 0.706205 0.502462 0.113641 0 1 1 0 0.304016 0.927146 0 0 0 0 0 1 0 1 0 0 0 0 0.972388 0 0 0 0.491595 0.000000 0 0.000000
234940 0.426431 0.506484 0.288642 0.818828 0 0 1 0 0.617757 0.984215 1 0 1 0 0 1 0 1 0 0 0 1 0.988216 0 0 0 0.421848 0.000000 1 0.000000
236051 0.445701 0.528093 0.790210 0.531623 0 0 1 0 0.305127 0.999749 0 0 1 0 0 1 0 1 1 0 1 0 0.958416 0 0 0 0.344429 0.194959 0 0.000000
30611 0.716030 0.546023 0.502462 0.479594 1 1 0 1 0.963179 0.996381 0 0 0 0 0 1 0 1 0 0 0 0 0.993728 1 0 0 0.134537 0.000000 0 0.000000
871 0.683097 0.511034 0.386333 0.851635 1 0 1 0 0.628734 0.999889 0 0 0 0 0 1 0 1 1 0 0 1 0.993904 1 1 1 1.000000 0.000000 0 0.000000
153082 0.181508 0.746300 0.502462 0.425761 0 0 0 1 0.829234 1.000000 0 0 1 0 0 1 0 1 1 0 0 1 0.997976 0 0 1 0.311695 0.000000 0 0.000000
188110 0.501046 0.725276 0.502462 0.488219 1 1 1 0 0.983048 0.966726 0 1 0 1 0 1 0 1 0 0 0 1 0.984323 0 0 1 0.276330 0.000000 0 0.000000
278046 0.288642 0.636376 0.502462 0.331116 0 0 1 0 0.617757 0.983546 1 0 1 0 0 1 0 1 1 0 1 0 0.979955 0 0 0 0.079177 0.000000 1 0.000000
26644 0.743533 0.081186 0.502462 0.787655 0 1 0 1 0.829790 0.932162 0 0 1 0 0 1 0 1 1 0 0 0 0.997189 0 0 0 0.491595 0.000000 0 0.000000
247686 0.505397 0.554947 0.502462 0.792334 0 1 1 0 0.524385 0.999972 0 0 1 0 0 1 0 1 0 0 0 0 0.984328 0 0 0 0.136490 0.000000 0 0.000000
115392 0.105422 0.691021 0.502462 0.839684 1 1 1 0 0.587328 1.000000 0 0 0 0 0 1 0 1 1 0 0 1 0.952106 0 0 0 0.268617 0.000000 0 0.000000
3950 0.486961 0.493863 0.502462 0.556595 0 1 1 0 0.395581 1.000000 1 1 1 1 0 1 0 1 1 1 0 0 0.970533 0 0 0 0.142153 0.000000 0 0.000000
54612 0.653632 0.662638 0.502462 0.531736 0 1 0 1 0.423232 0.902866 0 0 1 0 0 1 0 1 1 0 0 0 0.996338 0 0 0 0.421848 0.000000 0 0.000000
17380 0.762581 0.633032 0.502462 0.352818 1 1 1 0 0.649576 0.920186 0 0 0 0 0 1 0 1 1 0 0 1 0.996852 0 0 1 0.196267 0.000000 0 0.000000
146196 0.606717 0.382502 0.502462 0.416347 0 1 0 1 0.821870 0.965506 0 1 1 1 0 1 0 1 0 0 1 1 0.988264 0 0 1 0.141377 0.000000 0 0.000000
24352 0.568313 0.610991 0.354043 0.682751 0 1 0 1 0.396971 0.993711 0 0 1 0 0 1 0 1 1 0 0 0 0.992184 0 0 0 0.361433 0.000000 0 0.000000
188080 0.478874 0.511034 0.611665 0.777170 0 0 1 0 0.452411 0.911869 1 0 0 0 0 1 0 1 0 0 0 1 0.994991 0 0 1 0.136490 0.000000 1 0.000000
102436 0.511562 0.542445 0.502462 0.526268 1 0 1 0 0.402668 0.997011 1 0 0 0 0 1 0 1 0 0 1 0 0.986442 0 0 0 0.392880 0.000000 1 0.000000
158911 0.705507 0.397946 0.924966 0.507948 1 0 0 1 0.893567 0.945003 0 0 0 0 0 1 0 1 1 0 0 0 0.942511 1 1 0 1.000000 0.000000 0 0.000000
123277 0.563042 0.463275 0.371116 0.510879 0 0 1 0 0.378491 0.938801 0 0 1 0 1 0 1 0 1 0 0 0 0.964913 0 0 0 0.257000 0.000000 0 0.000000
114608 0.515009 0.511034 0.502462 0.403157 0 1 1 0 0.785744 0.989256 0 1 1 1 0 1 0 1 0 0 0 0 0.977041 0 0 0 0.024260 0.000000 0 0.000000
17870 0.511169 0.742182 0.411021 0.737148 0 0 1 0 0.381826 0.885468 0 1 0 1 0 1 0 1 1 0 0 1 0.982609 0 0 1 0.248124 0.000000 0 0.000000
197941 0.747103 0.716570 0.502462 0.568884 1 0 0 1 0.386411 0.806810 0 0 0 0 0 1 0 1 1 0 0 1 0.972068 1 1 1 1.000000 0.000000 0 0.000000
53438 0.465103 0.511034 0.502462 0.170688 0 0 1 0 0.326247 1.000000 0 0 1 0 1 0 1 0 0 0 0 0 0.985695 0 0 0 0.093190 0.000000 0 0.000000
64507 0.624017 0.709189 0.502462 0.443630 0 1 0 1 0.873975 0.963784 1 0 1 0 0 1 0 1 1 0 0 0 0.980224 0 0 0 0.421848 0.000000 1 0.000000
255439 0.570672 0.588488 0.388705 0.552537 0 1 0 1 0.391413 0.991250 1 1 1 1 0 1 0 1 1 0 0 0 0.979585 0 0 0 0.090864 0.000000 1 0.000000
218916 0.208960 0.511034 0.487548 0.612232 1 1 0 1 0.313047 0.949079 0 0 0 0 0 1 0 1 1 0 0 0 0.976628 0 0 0 0.344429 0.000000 0 0.000000
295147 0.456058 0.646330 0.502462 0.820462 0 1 1 0 0.535084 0.969956 0 0 1 0 0 1 0 1 0 0 1 1 0.977898 0 0 1 0.361433 0.000000 0 0.000000
182050 0.441130 0.598926 0.502462 0.081003 0 0 0 1 0.744755 0.984024 0 1 1 1 1 0 1 0 1 0 0 0 0.991880 0 0 0 0.276330 0.000000 0 0.000000
87096 0.199041 0.477649 0.502462 0.560992 0 1 0 1 0.297902 0.929881 1 0 1 0 0 1 0 1 1 0 0 0 0.964350 0 0 0 0.421848 0.000000 1 0.000000
172732 0.460710 0.513694 0.502462 0.113191 0 1 1 0 0.431569 0.979252 0 0 1 0 0 1 0 1 1 0 0 0 0.968683 0 0 0 0.344429 0.000000 0 0.000000
98310 0.670774 0.326475 0.502462 0.898647 1 1 0 1 0.786717 0.990950 0 1 0 1 0 1 0 1 1 0 0 0 0.993461 0 0 0 0.276330 0.000000 0 0.000000
288663 0.288822 0.353988 0.502462 0.895321 0 1 0 1 0.790885 0.965171 1 0 1 0 0 1 0 1 1 1 0 0 0.992660 0 0 0 0.268617 0.000000 0 0.000000
214467 0.116698 0.511034 0.498825 0.781454 0 1 0 1 0.474642 0.962343 1 0 1 0 0 1 0 1 0 0 0 0 0.944534 0 0 0 0.257000 0.000000 1 0.000000
247130 0.537984 0.549597 0.458548 0.761556 1 0 0 1 0.779769 0.969453 1 0 0 0 0 1 0 1 1 0 0 1 0.997642 0 0 1 0.096707 0.000000 1 0.000000
109604 0.759539 0.166406 0.502462 0.864713 0 1 0 1 0.652355 0.934737 1 0 1 0 0 1 0 1 1 1 1 0 0.975624 0 0 0 0.136490 0.000000 0 0.000000
73005 0.390561 0.511034 0.502462 0.270970 1 0 0 1 0.548701 0.992531 0 1 0 1 1 0 1 0 1 0 0 0 0.993778 0 0 0 0.024260 0.000000 0 0.000000
128669 0.186399 0.631355 0.480488 0.052255 0 0 0 1 0.422676 0.942146 0 0 1 0 1 0 1 0 1 0 0 1 0.980591 0 0 1 0.429796 0.000000 0 0.000000
296977 0.490977 0.810618 0.502462 0.378523 0 1 1 0 0.715437 0.958465 0 0 1 0 0 1 0 1 1 0 0 1 0.987516 0 0 1 0.309272 0.000000 0 0.000000
190097 0.488168 0.538863 0.430888 0.907723 1 1 1 0 0.823399 0.978254 1 1 0 1 0 1 0 1 1 1 0 1 0.981321 0 0 1 0.245631 0.000000 0 0.000000
130675 0.707534 0.511034 0.840726 0.216685 0 1 0 1 0.317354 1.000000 0 0 1 0 0 1 0 1 1 0 0 0 0.965215 0 0 0 0.491595 0.000000 0 0.000000
111766 0.446620 0.511034 0.774983 0.315220 0 0 0 1 0.557732 0.981092 1 0 0 0 0 1 0 1 1 0 0 1 0.981959 1 1 1 0.635991 0.000000 1 0.000000
28852 0.449783 0.511034 0.502462 0.914600 0 1 0 1 0.767125 0.956196 0 0 1 0 0 1 0 1 1 0 0 0 0.992669 0 0 0 0.129705 0.000000 0 0.000000
107963 0.752819 0.639708 0.502462 0.543968 0 1 0 1 0.355009 0.968804 1 0 1 0 0 1 0 1 0 1 0 0 0.962051 0 0 0 0.260475 0.000000 0 0.000000
89263 0.677184 0.413597 0.502462 0.245434 1 0 0 1 0.894539 0.917552 0 0 0 0 1 0 1 0 0 0 0 0 0.991825 0 0 0 0.491595 0.000000 0 0.000000
159031 0.243943 0.754406 0.502462 0.553664 1 1 0 1 0.955537 1.000000 0 1 0 1 0 1 0 1 1 0 0 1 0.949411 0 0 1 0.245631 0.000000 0 0.000000
156618 0.604209 0.408359 0.502462 0.608794 0 1 0 1 0.378352 0.993417 0 0 1 0 0 1 0 1 1 0 0 1 0.996281 0 0 1 0.147830 0.000000 0 0.000000
274042 0.124147 0.511034 0.502462 0.269222 0 0 1 0 0.597332 0.929881 0 0 1 0 0 1 0 1 1 0 0 1 0.991270 0 0 1 0.087776 0.000000 0 0.000000
9154 0.390332 0.678568 0.502462 0.564994 0 0 0 1 0.373767 0.947621 0 0 1 0 0 1 0 1 1 0 1 0 0.991663 0 0 0 0.158035 0.000000 0 0.000000
269933 0.412904 0.531686 0.750257 0.179425 0 0 0 1 0.805752 0.966689 0 0 1 0 1 0 1 0 0 0 0 1 0.889285 0 0 1 0.094492 0.000000 0 0.000000
19801 0.001707 0.511034 0.575900 0.423619 0 1 0 1 0.826317 0.945047 0 0 1 0 0 1 0 1 0 0 0 1 0.943722 0 0 0 0.392880 0.000000 0 0.000000
164848 0.714328 0.106156 0.332448 0.712401 1 0 1 0 0.919828 0.990257 0 0 0 0 0 1 0 1 0 1 0 0 0.998559 1 1 0 1.000000 0.000000 1 0.000000
61860 0.172073 0.675413 0.502462 0.211838 0 0 0 1 0.477004 0.957396 0 0 1 0 1 0 1 0 1 0 0 0 0.914491 0 0 0 0.109945 0.000000 0 0.000000
42512 0.733945 0.619528 0.502462 0.554059 1 0 0 1 0.329721 0.826295 0 0 0 0 0 1 0 1 0 0 0 0 0.982158 1 1 0 1.000000 0.000000 0 0.000000
177530 0.276948 0.511034 0.502462 0.747520 0 1 1 0 0.740725 0.986357 1 1 1 1 0 1 0 1 1 1 0 1 0.996822 0 0 0 0.024260 0.000000 0 0.000000
50358 0.377455 0.629674 0.076492 0.790079 0 1 1 0 0.499792 0.955471 1 0 1 0 0 1 0 1 1 1 1 0 0.980596 0 0 0 0.048783 0.000000 0 0.000000
134464 0.628950 0.404878 0.502462 0.953044 0 1 1 0 0.860358 0.968985 0 0 1 0 0 1 0 1 0 0 0 0 0.972690 0 0 0 0.094575 0.000000 0 0.000000
115885 0.582283 0.431192 0.373082 0.671815 0 1 1 0 0.872447 0.935073 0 0 1 0 0 1 0 1 0 0 0 1 0.985768 0 0 1 0.134537 0.194959 0 0.215338
229436 0.571068 0.656158 0.502462 0.786415 0 1 1 0 0.506878 0.943627 1 0 1 0 0 1 0 1 1 0 0 1 0.975097 0 0 1 0.158035 0.000000 1 0.000000
239717 0.624171 0.511034 0.502462 0.736528 0 1 0 1 0.390024 0.969345 1 0 1 0 0 1 0 1 1 0 0 0 0.995329 0 0 0 0.147830 0.000000 1 0.000000
129475 0.407297 0.232725 0.502462 0.362063 0 1 0 1 0.672086 0.925028 0 1 1 1 0 1 0 1 1 0 0 0 0.975075 0 0 0 0.142153 0.000000 0 0.000000
284087 0.237517 0.511034 0.502462 0.272492 0 0 0 1 0.540781 0.927198 0 1 1 1 1 0 1 0 1 0 0 0 0.984922 0 0 0 0.245631 0.000000 0 0.000000
35901 0.470292 0.661024 0.502462 0.364769 0 1 0 1 0.679589 0.983929 0 1 1 1 0 1 0 1 1 0 0 0 0.979809 0 0 0 0.276330 0.000000 0 0.000000
291686 0.711060 0.710674 0.747863 0.381229 0 1 0 1 0.718077 0.952608 0 0 1 0 0 1 0 1 1 0 1 1 0.957919 1 1 1 0.157398 0.000000 0 0.000000
200245 0.408411 0.659406 0.756371 0.244814 0 1 0 1 0.441017 0.989651 0 1 1 1 0 1 0 1 1 0 0 0 0.988874 0 0 0 0.024260 0.000000 0 0.000000
215522 0.653548 0.614414 0.502462 0.893236 0 1 0 1 0.811588 0.981353 0 0 0 0 0 1 0 1 1 0 0 1 0.949481 0 0 1 0.158035 0.000000 0 0.000000
272400 0.406057 0.746300 0.571282 0.428749 0 1 0 1 0.836738 0.870485 0 0 1 0 0 1 0 1 1 0 0 1 0.990274 0 0 1 0.147830 0.000000 0 0.000000
235942 0.433349 0.213967 0.502462 0.224126 0 0 0 1 0.812978 0.982712 0 0 0 0 1 0 1 0 0 0 0 0 0.978231 0 0 0 0.421848 0.000000 0 0.000000
212595 0.571732 0.528093 0.167642 0.787148 0 1 1 0 0.507850 0.899234 1 0 1 0 0 1 0 1 1 1 1 0 0.974193 0 0 0 0.268617 0.000000 0 0.000000
243900 0.632993 0.424130 0.502462 0.401240 0 0 0 1 0.771016 1.000000 0 0 1 0 0 1 0 1 1 0 0 0 0.961534 0 0 0 0.421848 0.000000 0 0.000000
60106 0.601183 0.636376 0.338494 0.666516 0 1 0 1 0.265805 0.936881 1 0 1 0 0 1 0 1 1 1 0 1 0.961796 0 0 1 0.344429 0.000000 0 0.000000
98450 0.230590 0.368969 0.502462 0.089121 1 0 0 1 0.432958 0.955390 0 1 0 1 1 0 1 0 1 0 0 0 0.956239 0 0 0 0.038481 0.000000 0 0.000000
253524 0.685619 0.122180 0.801598 0.311330 0 0 0 1 0.641934 0.959563 0 0 1 0 1 0 1 0 0 0 0 1 0.978252 1 1 1 0.446855 0.000000 0 0.000000
34303 0.648336 0.689479 0.502462 0.424803 0 0 0 1 0.827428 1.000000 0 0 1 0 0 1 0 1 1 0 1 0 0.956469 0 0 0 0.491595 0.000000 0 0.000000
33986 0.690678 0.629674 0.502462 0.221759 0 1 1 0 0.328887 0.999944 0 1 1 1 0 1 0 1 1 0 0 0 0.995773 0 0 0 0.248124 0.000000 0 0.000000
307328 0.581443 0.511034 0.502462 0.654848 0 1 1 0 0.704460 0.957077 0 0 1 0 0 1 0 1 1 0 1 1 0.982330 0 0 1 0.421848 0.000000 0 0.000000
127384 0.596765 0.695622 0.502462 0.062345 0 0 0 1 0.390024 0.979019 0 0 0 0 1 0 1 0 0 0 0 1 0.995560 0 0 1 0.066382 0.000000 0 0.000000
271288 0.320505 0.695622 0.502462 0.416798 0 0 1 0 0.807976 1.000000 0 0 1 0 1 0 1 0 1 0 0 1 0.997167 0 0 1 0.134537 0.000000 0 0.000000
295490 0.677771 0.511034 0.502462 0.191995 0 1 1 0 0.293734 1.000000 0 0 0 0 0 1 0 1 1 0 0 0 0.929778 0 0 0 0.093190 0.000000 0 0.000000
278189 0.652904 0.323311 0.494823 0.799718 0 1 0 1 0.560928 0.975517 0 0 0 0 0 1 0 1 1 0 0 1 0.974154 0 0 1 0.088357 0.000000 0 0.000000
34614 0.618868 0.511034 0.502462 0.943517 0 1 0 1 0.944560 0.995921 1 0 1 0 0 1 0 1 0 0 0 0 0.984700 0 0 0 0.361433 0.000000 1 0.000000
149615 0.478214 0.673830 0.502462 0.192277 0 0 0 1 0.328748 0.991877 0 0 1 0 1 0 1 0 0 1 0 0 0.987910 0 0 0 0.093190 0.000000 0 0.000000
77443 0.688436 0.798137 0.628136 0.812740 0 0 1 0 0.520078 0.847015 0 0 1 0 0 1 0 1 1 0 0 1 0.972897 0 0 1 0.491595 0.000000 0 0.000000
179564 0.542177 0.375711 0.502462 0.160090 0 0 0 1 0.383771 0.990438 0 0 1 0 1 0 1 0 1 0 0 1 0.938172 0 0 0 0.429796 0.000000 0 0.000000
150275 0.409611 0.520898 0.502462 0.771477 0 1 0 1 0.544671 0.969669 1 0 1 0 0 1 0 1 1 0 0 0 0.981516 0 0 0 0.254009 0.000000 1 0.000000
148777 0.255051 0.511034 0.502462 0.640586 0 1 1 0 0.347506 0.937352 0 0 1 0 0 1 0 1 1 0 0 0 0.961327 0 0 0 0.491595 0.000000 0 0.000000
101942 0.720975 0.511034 0.502462 0.283258 0 0 1 0 0.481173 0.918381 0 0 1 0 0 1 0 1 1 0 1 1 0.976998 0 0 1 0.361433 0.000000 0 0.000000
272225 0.341015 0.755740 0.502462 0.447069 1 0 0 1 0.883285 1.000000 1 0 0 0 0 1 0 1 1 0 0 0 0.949375 0 0 0 0.361433 0.000000 1 0.000000
66673 0.498837 0.569149 0.502462 0.640192 0 1 1 0 0.393636 0.980700 0 0 1 0 0 1 0 1 1 0 0 1 0.999558 0 0 1 0.206749 0.000000 0 0.000000
147090 0.703253 0.584990 0.502462 0.700564 1 0 1 0 0.394748 0.998768 1 0 0 0 0 1 0 1 0 0 0 1 0.999668 1 1 1 0.635991 0.000000 1 0.000000
77204 0.425666 0.747663 0.568502 0.842559 0 1 1 0 0.632208 1.000000 1 0 1 0 0 1 0 1 0 1 0 0 0.977510 1 1 0 0.157398 0.000000 0 0.000000
233326 0.220650 0.353988 0.688064 0.171364 0 0 0 1 0.257190 0.955350 0 1 1 1 1 0 1 0 0 0 0 0 0.992646 0 0 0 0.097136 0.000000 0 0.000000
205036 0.437317 0.251239 0.647650 0.201917 0 0 0 1 0.386272 1.000000 0 1 1 1 1 0 1 0 1 0 0 0 0.963765 0 0 0 0.248124 0.000000 0 0.000000
178930 0.641827 0.694093 0.502462 0.673055 1 0 0 1 0.774073 0.969776 1 0 0 0 0 1 0 1 1 1 0 1 0.984848 0 0 1 0.097483 0.000000 0 0.000000
56363 0.584197 0.511034 0.502462 0.287711 1 0 0 1 0.569821 0.953884 0 0 0 0 0 1 0 1 1 0 0 0 0.999817 0 0 0 0.064665 0.000000 0 0.000000
53669 0.106549 0.511034 0.502462 0.962401 0 1 1 0 0.883007 0.975994 1 0 0 0 0 1 0 1 1 1 0 0 0.993416 0 0 0 0.491595 0.000000 0 0.000000
302784 0.005740 0.846378 0.509937 0.538331 0 1 0 1 0.407670 1.000000 0 0 1 0 0 1 0 1 1 0 0 0 0.967495 0 0 0 0.268617 0.194959 0 0.215338
132760 0.192316 0.511892 0.502462 0.301522 0 0 0 1 0.677505 0.948609 0 0 1 0 0 1 0 1 1 0 0 0 0.953600 0 0 0 0.093190 0.000000 0 0.000000
288521 0.677590 0.511034 0.502462 0.688444 0 1 1 0 0.651938 0.980537 1 0 1 0 0 1 0 1 1 1 0 1 0.985783 0 0 1 0.196267 0.000000 0 0.000000
261341 0.738780 0.687933 0.369809 0.861950 0 0 1 0 0.635543 0.922605 0 1 1 1 0 1 0 1 1 0 0 0 0.993561 0 0 0 0.276330 0.000000 0 0.000000
243593 0.349582 0.511034 0.502462 0.383484 0 0 0 1 0.731555 0.997011 0 0 1 0 1 0 1 0 0 0 0 1 0.995543 1 1 1 0.045016 0.000000 0 0.000000
266528 0.193409 0.283712 0.556335 0.441883 0 0 0 1 0.871613 0.993417 0 1 1 1 0 1 0 1 1 0 0 1 0.970430 0 0 1 0.024260 0.000000 0 0.000000
90544 0.438797 0.722393 0.606939 0.603777 1 1 1 0 0.447409 0.944206 0 1 0 1 0 1 0 1 1 0 0 0 0.996782 0 0 0 0.276330 0.194959 0 0.215338
34136 0.303049 0.392774 0.502462 0.250620 0 0 0 1 0.400584 0.864842 0 0 1 0 1 0 1 0 1 0 0 0 0.980291 0 0 0 0.491595 0.000000 0 0.000000
42832 0.657537 0.511892 0.413462 0.820744 1 0 0 1 0.903571 0.980733 1 0 0 0 0 1 0 1 0 1 0 1 0.975651 0 0 1 0.134897 0.000000 0 0.000000
144727 0.267025 0.609276 0.282714 0.813923 1 0 0 1 0.647770 0.985794 0 1 0 1 0 1 0 1 0 0 0 0 0.983063 0 0 0 0.107702 0.000000 0 0.000000
240325 0.723055 0.397946 0.599863 0.229312 0 0 0 1 0.349034 0.959719 0 0 1 0 1 0 1 0 1 0 0 1 0.996716 0 0 1 0.392880 0.000000 0 0.000000
119732 0.698771 0.272134 0.436843 0.545265 0 1 0 1 0.413784 0.940325 0 0 1 0 0 1 0 1 1 0 0 1 0.966501 1 1 1 0.045016 0.000000 0 0.000000
24487 0.627132 0.511034 0.502462 0.186077 0 0 0 1 0.433653 0.993505 0 0 1 0 1 0 1 0 1 0 0 0 0.930729 0 0 0 0.361433 0.000000 0 0.000000
48665 0.579755 0.392774 0.502462 0.529763 1 0 0 1 0.321106 0.960922 0 1 0 1 0 1 0 1 1 0 0 1 0.967159 0 0 1 0.141377 0.000000 0 0.000000
150432 0.651986 0.468660 0.502462 0.785287 0 1 0 1 0.446436 0.848705 0 0 1 0 0 1 0 1 1 0 1 0 0.995262 0 0 0 0.429796 0.000000 0 0.000000
267262 0.423475 0.340906 0.502462 0.599605 0 1 0 1 0.375712 0.960186 0 0 1 0 0 1 0 1 0 0 0 1 0.958674 0 0 1 0.096707 0.000000 0 0.000000
155284 0.657665 0.581484 0.502462 0.254340 0 0 0 1 0.483396 0.921482 0 0 1 0 1 0 1 0 0 0 0 0 0.977998 0 0 0 0.197042 0.000000 0 0.000000
206231 0.295501 0.511034 0.827174 0.012120 0 0 0 1 0.449354 0.947275 0 1 1 1 1 0 1 0 1 0 0 1 0.933692 0 0 1 0.039215 0.000000 0 0.000000
79659 0.250618 0.191822 0.502462 0.093799 0 0 0 1 0.438516 0.964461 0 0 1 0 1 0 1 0 0 0 0 1 0.994332 0 0 0 0.268617 0.309003 0 0.341303
105861 0.596118 0.540654 0.502462 0.076156 0 0 0 1 0.316938 1.000000 0 0 1 0 1 0 1 0 0 0 0 0 0.998253 0 0 0 0.421848 0.000000 0 0.000000
60905 0.183960 0.816092 0.662243 0.479425 1 0 0 1 0.998194 0.947750 0 1 0 1 1 0 1 0 0 0 0 0 0.988231 0 0 0 0.024260 0.000000 0 0.000000
97973 0.604419 0.511034 0.648761 0.321364 1 1 1 0 0.573572 0.975892 1 0 0 0 0 1 0 1 1 0 0 1 0.986038 0 0 1 0.344429 0.000000 1 0.000000
77723 0.722012 0.294083 0.502462 0.891488 0 1 0 1 0.711130 1.000000 0 0 1 0 0 1 0 1 1 0 0 1 0.991340 0 0 1 0.361433 0.194959 0 0.215338
21849 0.700246 0.511034 0.570431 0.359865 0 1 1 0 0.691538 0.981451 0 0 0 0 0 1 0 1 0 0 0 1 0.986797 0 0 1 0.268617 0.000000 0 0.000000
152601 0.515752 0.081726 0.502462 0.803551 0 0 1 0 0.520078 0.948095 1 0 1 0 0 1 0 1 1 1 0 0 0.984139 0 0 0 0.309272 0.194959 0 0.215338
58344 0.564591 0.474051 0.502462 0.473281 0 0 0 1 0.947617 0.949929 0 0 1 0 0 1 0 1 1 0 0 0 0.959456 0 0 0 0.115414 0.000000 0 0.000000
304943 0.481352 0.286652 0.502462 0.525536 0 1 0 1 0.627206 1.000000 1 0 1 0 0 1 0 1 1 0 0 0 0.969609 1 1 1 0.635991 0.000000 1 0.000000
117530 0.569203 0.418854 0.502462 0.373112 0 0 0 1 0.781437 1.000000 0 0 0 0 0 1 0 1 1 0 0 1 0.967319 0 0 1 0.392880 0.000000 0 0.000000
208339 0.277535 0.511034 0.502462 0.279481 0 1 1 0 0.524385 1.000000 0 0 1 0 0 1 0 1 1 0 0 0 0.932911 0 0 0 0.392880 0.000000 0 0.000000
305626 0.686251 0.733815 0.571351 0.517700 0 0 0 1 0.314298 0.998740 0 0 1 0 0 1 0 1 1 0 0 1 0.955571 1 1 1 1.000000 0.000000 0 0.000000
9155 0.535820 0.777659 0.142477 0.752255 1 0 1 0 0.367097 0.999889 0 0 0 0 0 1 0 1 1 0 0 1 0.998335 1 1 1 0.157398 0.000000 0 0.000000
207930 0.677619 0.535276 0.502462 0.100676 0 0 0 1 0.370154 1.000000 0 0 1 0 1 0 1 0 0 0 0 1 0.954181 1 1 1 1.000000 0.000000 0 0.000000
93512 0.293090 0.704706 0.502462 0.572322 0 1 0 1 0.298041 0.921106 0 0 1 0 0 1 0 1 1 0 0 1 0.990246 0 0 1 0.344429 0.000000 0 0.000000
273246 0.355947 0.511034 0.502462 0.238501 0 1 1 0 0.739614 0.990799 0 0 1 0 0 1 0 1 1 0 0 0 0.972662 0 0 0 0.421848 0.194959 0 0.000000
140715 0.490436 0.363945 0.490305 0.157554 0 1 0 1 0.388356 0.967715 0 0 1 0 0 1 0 1 1 0 1 1 0.946108 0 0 1 0.129705 0.000000 0 0.000000
132778 0.040029 0.396220 0.369844 0.606313 1 0 0 1 0.302626 0.892856 0 0 0 0 0 1 0 1 1 0 0 1 0.971888 0 0 1 0.421848 0.000000 0 0.000000
198173 0.696931 0.511034 0.502462 0.313191 0 0 0 1 0.558010 0.952110 0 0 1 0 0 1 0 1 1 0 0 1 0.956193 0 0 1 0.129331 0.000000 0 0.000000
292505 0.630164 0.643026 0.502462 0.285118 0 0 0 1 0.498263 0.874689 0 0 1 0 1 0 1 0 1 0 0 1 0.982010 1 1 1 0.157398 0.000000 0 0.000000
255106 0.243182 0.272134 0.502462 0.866516 0 1 1 0 0.646936 1.000000 1 0 1 0 0 1 0 1 1 1 0 0 0.977755 0 0 0 0.361433 0.000000 0 0.000000
138610 0.170726 0.511034 0.502462 0.881793 1 1 0 1 0.684452 0.910287 0 0 0 0 0 1 0 1 0 0 0 1 0.999049 0 0 1 0.128209 0.000000 0 0.000000
306173 0.754151 0.436506 0.502462 0.340474 0 1 1 0 0.626928 0.971873 0 0 1 0 0 1 0 1 1 0 0 1 0.951748 1 1 1 0.635991 0.000000 0 0.000000
220265 0.561621 0.529890 0.569087 0.419673 0 1 0 1 0.815201 0.999609 0 0 1 0 0 1 0 1 0 0 0 1 0.970789 0 0 1 0.129705 0.000000 0 0.000000
51256 0.414113 0.511034 0.567654 0.386753 0 1 0 1 0.732944 0.995950 0 0 1 0 0 1 0 1 0 0 1 1 0.999587 0 0 0 0.226010 0.000000 0 0.000000
201732 0.656670 0.511034 0.502462 0.684386 1 0 0 1 0.986383 0.980110 0 0 0 0 0 1 0 1 0 0 0 1 0.965564 1 1 1 0.446855 0.000000 0 0.000000
288536 0.683996 0.267869 0.502462 0.107610 0 0 0 1 0.893984 0.979813 0 0 1 0 1 0 1 0 0 0 0 1 0.969995 0 0 1 0.079177 0.000000 0 0.000000
112307 0.150884 0.304672 0.502462 0.594589 0 0 1 0 0.295957 1.000000 0 1 1 1 0 1 0 1 0 0 1 1 0.954342 0 0 1 0.107702 0.000000 0 0.000000
77721 0.313437 0.547810 0.764776 0.093292 0 0 0 1 0.303321 0.961884 0 1 1 1 1 0 1 0 1 0 0 0 0.949925 0 0 0 0.282797 0.000000 0 0.000000
172105 0.566617 0.641368 0.502462 0.377452 0 0 1 0 0.716965 0.972681 1 0 1 0 0 1 0 1 0 1 0 1 0.997124 0 0 1 0.361433 0.309003 0 0.215338
116818 0.267183 0.102119 0.502462 0.160710 0 0 0 1 0.394192 0.978621 0 0 1 0 1 0 1 0 0 0 0 1 0.963004 0 0 1 0.421848 0.000000 0 0.000000
180936 0.127223 0.621226 0.454342 0.415163 0 1 0 1 0.802835 1.000000 0 0 1 0 0 1 0 1 1 0 1 0 0.999996 0 0 0 0.206749 0.000000 0 0.000000
120658 0.431621 0.574447 0.502462 0.874915 1 0 0 1 0.775601 0.986357 1 0 0 0 0 1 0 1 1 0 0 1 0.995236 0 0 1 0.311695 0.000000 1 0.000000
118024 0.271646 0.340906 0.502462 0.885344 1 1 1 0 0.695151 0.987104 0 1 0 1 0 1 0 1 0 0 1 0 0.996699 0 0 0 0.090864 0.000000 0 0.000000
112504 0.606033 0.766234 0.770241 0.397407 0 0 0 1 0.761984 0.933481 0 0 1 0 0 1 0 1 1 0 0 0 0.987000 0 0 0 0.096707 0.000000 0 0.000000
67980 0.663290 0.461482 0.741147 0.113191 0 0 0 1 0.435320 0.980405 0 0 1 0 1 0 1 0 1 0 0 1 0.968409 0 0 1 0.392880 0.000000 0 0.000000
112916 0.712285 0.609276 0.244460 0.780327 0 0 1 0 0.437821 0.978887 0 0 0 0 0 1 0 1 1 1 0 0 0.991280 1 1 0 0.635991 0.000000 0 0.000000
101533 0.658143 0.511034 0.502462 0.542334 0 0 0 1 0.352508 0.939404 0 0 1 0 0 1 0 1 1 0 1 1 0.952545 0 0 1 0.336758 0.194959 0 0.215338
104147 0.300152 0.511034 0.345685 0.145998 0 0 0 1 0.385577 0.974694 0 0 1 0 1 0 1 0 0 0 0 1 0.965941 0 0 1 0.048852 0.000000 0 0.000000
76686 0.403907 0.180888 0.502462 0.436359 0 1 1 0 0.857024 0.991429 1 0 1 0 0 1 0 1 1 0 1 0 0.980354 0 0 0 0.309272 0.000000 1 0.000000
207436 0.479747 0.511034 0.366611 0.689515 0 0 0 1 0.442546 0.994472 0 0 0 0 0 1 0 1 1 0 0 0 0.964907 0 0 0 0.421848 0.000000 0 0.000000
172169 0.562424 0.349055 0.502462 0.274183 0 0 0 1 0.560372 0.973135 0 1 1 1 1 0 1 0 1 0 0 1 0.998624 0 0 1 0.248124 0.000000 0 0.000000
143284 0.114053 0.761026 0.502462 0.545998 0 1 1 0 0.430318 0.995054 0 0 1 0 0 1 0 1 0 0 0 1 0.958126 0 0 1 0.147830 0.000000 0 0.000000
210003 0.634937 0.753067 0.502462 0.426832 0 0 1 0 0.833959 0.977146 0 0 1 0 1 0 1 0 1 0 0 1 0.987824 0 0 1 0.226010 0.000000 0 0.000000
195107 0.736444 0.511034 0.439958 0.380665 0 1 1 0 0.744338 0.975380 0 0 1 0 0 1 0 1 1 0 1 0 0.963434 0 0 0 0.093190 0.000000 0 0.000000
289874 0.490988 0.349055 0.502462 0.720237 0 1 1 0 0.552452 1.000000 1 0 1 0 0 1 0 1 0 0 1 0 0.968596 0 0 0 0.309272 0.000000 1 0.000000
152680 0.141585 0.108924 0.405245 0.583878 0 1 1 0 0.363902 0.891138 0 0 1 0 0 1 0 1 1 0 1 1 0.961252 0 0 1 0.429796 0.000000 0 0.000000
120326 0.346877 0.751724 0.803791 0.636809 1 1 0 1 0.501876 0.968442 0 0 0 0 0 1 0 1 1 0 0 0 0.998946 0 0 0 0.344429 0.000000 0 0.000000
269356 0.090962 0.454321 0.395232 0.739402 0 0 1 0 0.358066 1.000000 0 1 1 1 0 1 0 1 1 0 0 1 0.988569 0 0 1 0.141377 0.000000 0 0.000000
287558 0.628955 0.483050 0.433595 0.639177 0 0 0 1 0.545505 0.995286 0 0 1 0 0 1 0 1 1 0 1 1 0.997703 0 0 1 0.138276 0.000000 0 0.000000
250086 0.425174 0.511034 0.149770 0.489966 0 1 0 1 0.991107 1.000000 0 0 1 0 0 1 0 1 1 0 0 1 0.998171 0 0 1 0.361433 0.000000 0 0.000000
26857 0.531036 0.258084 0.502462 0.332413 0 1 1 0 0.610810 0.910875 0 0 1 0 0 1 0 1 1 0 1 1 0.996224 0 0 1 0.392880 0.000000 0 0.000000
84845 0.029389 0.511034 0.502462 0.473393 0 1 1 0 0.946367 0.976334 1 1 1 1 0 1 0 1 1 0 1 0 0.998123 0 0 0 0.248124 0.000000 1 0.000000
52157 0.406336 0.607557 0.502462 0.908117 0 0 1 0 0.750174 0.982422 1 0 0 0 0 1 0 1 1 1 0 0 0.981582 0 0 0 0.206749 0.000000 0 0.000000
211319 0.626451 0.372334 0.502462 0.164600 0 0 0 1 0.428373 0.958109 0 0 1 0 1 0 1 0 1 0 0 0 0.979986 0 0 0 0.392880 0.000000 0 0.000000
137097 0.456166 0.542445 0.502462 0.078523 0 0 0 1 0.437127 1.000000 0 0 1 0 1 0 1 0 0 0 0 0 0.995329 0 0 0 0.257000 0.000000 0 0.000000
209872 0.650983 0.324891 0.502462 0.283766 1 0 0 1 0.743504 0.931521 0 0 0 0 0 1 0 1 1 0 0 1 0.996299 0 0 1 0.197042 0.000000 0 0.000000
301770 0.393355 0.501075 0.502462 0.354453 0 1 0 1 0.666250 0.913085 1 1 1 1 0 1 0 1 1 1 1 0 0.979329 0 0 0 0.245631 0.000000 0 0.000000
288760 0.689851 0.697147 0.557489 0.854171 1 1 0 1 0.633875 0.985950 0 0 0 0 0 1 0 1 1 0 0 1 0.991284 0 0 1 0.079177 0.000000 0 0.000000
48988 0.510644 0.659406 0.502462 0.360316 1 1 0 1 0.671391 0.955228 0 1 0 1 0 1 0 1 1 0 0 0 0.944363 0 0 0 0.245631 0.000000 0 0.000000
289763 0.653716 0.612704 0.698067 0.176494 0 0 0 1 0.384049 0.982841 0 0 1 0 1 0 1 0 1 0 0 0 0.964889 0 0 0 0.491595 0.000000 0 0.000000
27937 0.665620 0.269286 0.746170 0.080834 0 1 0 1 0.324163 0.925600 0 0 1 0 0 1 0 1 1 0 0 0 0.921959 0 0 0 0.421848 0.000000 0 0.000000
166121 0.771200 0.712155 0.648348 0.073844 0 0 1 0 0.366403 0.857826 0 0 1 0 1 0 1 0 1 0 0 1 0.924586 0 0 1 0.226010 0.000000 0 0.000000
232137 0.710250 0.709189 0.427683 0.701466 0 0 0 1 0.585939 0.938009 0 0 1 0 0 1 0 1 1 0 1 1 0.995231 0 0 1 0.421848 0.000000 0 0.000000
157941 0.564338 0.654529 0.652502 0.455186 0 1 0 1 0.902459 0.852208 0 1 1 1 0 1 0 1 1 0 0 0 0.963948 0 0 0 0.248124 0.000000 0 0.000000
99266 0.560027 0.306202 0.377689 0.563247 0 1 0 1 0.449354 0.966578 0 0 1 0 0 1 0 1 1 0 0 1 0.962250 0 0 1 0.081932 0.000000 0 0.000000
110569 0.595702 0.312365 0.502462 0.659639 1 0 1 0 0.877449 0.999833 0 0 0 0 0 1 0 1 0 0 0 1 0.988769 0 0 1 0.226010 0.000000 0 0.000000
82796 0.405550 0.722393 0.502462 0.167136 0 0 0 1 0.392664 0.990649 0 0 1 0 1 0 1 0 0 0 0 1 0.942025 0 0 1 0.118752 0.000000 0 0.000000
198461 0.130079 0.812823 0.606110 0.686077 1 0 0 1 0.614145 1.000000 0 0 0 0 0 1 0 1 1 0 0 1 0.999019 0 0 1 0.491595 0.000000 0 0.000000
137600 0.316656 0.511034 0.502462 0.763811 0 1 0 1 0.560928 0.869908 0 1 1 1 0 1 0 1 1 0 0 1 0.974253 0 0 1 0.276330 0.000000 0 0.000000
135401 0.565693 0.583238 0.308632 0.795998 1 1 1 0 0.784494 0.966099 1 0 0 0 0 1 0 1 1 0 1 0 0.998572 0 0 0 0.268617 0.000000 1 0.000000
32355 0.648356 0.380800 0.502462 0.201409 0 0 0 1 0.358622 0.939542 0 0 1 0 1 0 1 0 0 1 0 0 0.939468 0 0 0 0.344429 0.000000 0 0.000000
230153 0.627806 0.205598 0.497917 0.628579 0 1 1 0 0.416146 0.943984 0 0 1 0 0 1 0 1 1 0 0 0 0.960730 0 0 0 0.491595 0.309003 0 0.000000
96807 0.268663 0.759712 0.363247 0.452988 0 1 1 0 0.902459 0.999972 1 0 1 0 0 1 0 1 1 0 1 1 0.968526 0 0 1 0.094492 0.000000 1 0.000000
171943 0.443314 0.816092 0.366408 0.623675 0 1 1 0 0.179658 0.983897 0 0 1 0 0 1 0 1 0 0 0 1 0.998620 0 0 1 0.311695 0.000000 0 0.000000
196850 0.574541 0.565608 0.585259 0.541037 1 0 1 0 0.344310 1.000000 0 0 0 0 0 1 0 1 1 0 0 0 0.992897 0 0 0 0.093190 0.000000 0 0.000000
81644 0.592992 0.673830 0.488315 0.172097 0 0 0 1 0.368904 0.935217 0 1 1 1 1 0 1 0 0 0 0 1 0.990629 0 0 1 0.038481 0.000000 0 0.000000
71169 0.609281 0.700184 0.502462 0.670180 0 1 1 0 0.384327 0.999972 0 0 1 0 0 1 0 1 0 0 0 0 0.993805 0 0 0 0.309272 0.000000 0 0.000000
92030 0.267316 0.595456 0.688336 0.125085 0 0 0 1 0.345839 0.991996 0 0 1 0 1 0 1 0 0 0 0 0 0.991516 0 0 0 0.392880 0.000000 0 0.000000
163623 0.348091 0.511034 0.228615 0.820857 0 1 1 0 0.996248 0.969848 0 0 1 0 0 1 0 1 1 0 1 1 0.999894 0 0 1 0.260475 0.194959 0 0.215338
80188 0.636896 0.511034 0.502462 0.700113 0 0 1 0 0.377518 0.935790 1 0 1 0 0 1 0 1 0 0 0 0 0.966911 0 0 0 0.421848 0.000000 1 0.000000
253701 0.588128 0.486653 0.520878 0.894138 1 1 0 1 0.727803 0.999889 0 0 0 0 0 1 0 1 1 0 0 1 0.997538 0 0 1 0.311695 0.000000 0 0.000000
49531 0.317764 0.511034 0.502462 0.260372 0 0 0 1 0.431986 0.981255 0 0 1 0 1 0 1 0 1 0 0 1 0.970424 0 0 1 0.344429 0.000000 0 0.000000
95855 0.682002 0.353988 0.502462 0.274464 0 1 1 0 0.507712 0.961423 0 0 1 0 0 1 0 1 1 0 1 1 0.949559 0 0 1 0.134897 0.194959 0 0.215338
29159 0.163668 0.368969 0.502462 0.788895 1 1 0 1 0.500347 1.000000 0 1 0 1 0 1 0 1 1 0 0 1 0.993379 0 0 1 0.065247 0.000000 0 0.000000
306498 0.600978 0.647977 0.882463 0.223619 1 0 0 1 0.346255 0.984849 1 0 0 0 0 1 0 1 1 0 0 0 0.991331 0 0 0 0.309272 0.000000 1 0.000000
168319 0.221422 0.544235 0.398556 0.528298 0 1 1 0 0.453383 0.978621 0 0 1 0 0 1 0 1 1 0 0 1 0.981720 0 0 1 0.097483 0.000000 0 0.000000
204908 0.554859 0.511034 0.284806 0.217193 0 1 1 0 0.443657 0.933675 1 0 1 0 0 1 0 1 1 1 0 0 0.925327 0 0 0 0.491595 0.000000 1 0.000000
129134 0.378334 0.265049 0.631989 0.562514 0 1 0 1 0.838266 0.920132 0 1 1 1 0 1 0 1 1 0 0 1 0.950855 0 0 1 0.282797 0.000000 0 0.000000
247729 0.539119 0.793449 0.502462 0.296449 0 1 1 0 0.513825 0.967788 0 0 1 0 0 1 0 1 1 0 1 0 0.985440 0 0 0 0.421848 0.000000 0 0.000000
44818 0.527331 0.441836 0.502462 0.252368 0 0 1 0 0.517021 0.950983 0 1 1 1 1 0 1 0 0 0 0 0 0.982193 0 0 0 0.276330 0.000000 0 0.000000
80098 0.409061 0.730987 0.641819 0.333033 1 1 1 0 0.626650 0.974349 0 0 0 0 0 1 0 1 0 0 0 1 0.984987 0 0 1 0.336758 0.194959 0 0.000000
63516 0.490578 0.362277 0.381186 0.832244 1 1 0 1 0.667361 0.990227 0 0 0 0 0 1 0 1 1 0 0 0 0.999045 0 0 0 0.067213 0.000000 0 0.000000
212654 0.499685 0.454321 0.502462 0.557948 0 0 0 1 0.965402 0.921643 1 0 1 0 0 1 0 1 1 0 0 0 0.948816 1 1 0 0.635991 0.000000 1 0.000000
202974 0.752411 0.222581 0.856315 0.472266 0 1 1 0 0.947200 0.938055 0 0 1 0 0 1 0 1 1 0 1 1 0.974440 0 0 1 0.309272 0.000000 0 0.000000
205318 0.705366 0.712155 0.502462 0.197520 0 0 0 1 0.278310 0.975449 0 1 1 1 0 1 0 1 1 1 0 0 0.964744 0 0 0 0.038481 0.000000 1 0.000000
163661 0.643846 0.511034 0.502462 0.513923 0 0 1 0 0.441573 0.987413 1 0 1 0 0 1 0 1 1 0 0 0 0.956502 1 1 0 0.635991 0.000000 1 0.000000
176811 0.385462 0.355639 0.502462 0.126550 1 0 0 1 0.333889 0.986419 0 0 0 0 1 0 1 0 1 0 0 1 0.963416 0 0 1 0.309272 0.000000 0 0.000000
219430 0.720640 0.511034 0.342692 0.794081 0 1 0 1 0.466861 1.000000 0 0 1 0 0 1 0 1 1 0 0 1 0.979861 1 1 1 0.635991 0.194959 0 0.215338
1368 0.260856 0.706205 0.502462 0.496843 0 1 1 0 0.421009 0.919860 0 0 1 0 0 1 0 1 1 0 1 1 0.956476 0 0 1 0.069553 0.000000 0 0.000000
259107 0.683622 0.673830 0.502462 0.659357 1 1 1 0 0.625538 0.943180 1 0 0 0 0 1 0 1 0 1 0 1 0.961815 0 0 1 0.147830 0.000000 0 0.000000
224518 0.615360 0.562060 0.502462 0.203326 0 0 0 1 0.370432 0.974935 0 0 1 1 1 0 1 0 0 0 0 1 0.979010 0 0 1 0.282797 0.000000 0 0.000000
106822 0.714377 0.236611 0.502462 0.636359 0 0 0 1 0.571905 0.977953 0 0 1 0 0 1 0 1 1 0 0 1 0.986238 1 1 0 0.446855 0.000000 0 0.000000
224548 0.303456 0.662638 0.502462 0.190304 0 0 0 1 0.279561 0.951234 0 0 1 0 1 0 1 0 1 0 0 1 0.982015 0 0 1 0.429796 0.194959 0 0.215338
175438 0.779311 0.422370 0.332291 0.705693 0 1 1 0 0.343338 0.898194 0 0 1 0 0 1 0 1 0 0 0 1 0.991382 1 1 1 0.635991 0.000000 0 0.000000
302357 0.715002 0.499272 0.558847 0.726494 1 1 1 0 0.386828 0.966689 0 0 0 0 0 1 0 1 0 0 0 0 0.980571 0 0 0 0.136490 0.000000 0 0.000000
287159 0.508311 0.394495 0.320763 0.801015 1 1 0 1 0.787134 0.992501 0 0 0 0 0 1 0 1 1 0 0 0 0.956909 0 0 0 0.206749 0.000000 0 0.000000
63740 0.684246 0.740799 0.502462 0.184611 0 0 0 1 0.332083 1.000000 0 0 1 0 1 0 1 0 1 0 0 1 0.924487 0 0 1 0.336758 0.000000 0 0.000000
136381 0.402749 0.190706 0.502462 0.905299 0 1 0 1 0.744616 0.994443 0 0 0 0 0 1 0 1 1 0 0 1 0.999100 0 0 0 0.268617 0.000000 0 0.000000
186925 0.565661 0.470456 0.502462 0.387542 0 1 0 1 0.793525 0.963331 0 0 1 0 0 1 0 1 1 0 0 1 0.950966 0 0 1 0.134537 0.000000 0 0.000000
224592 0.658128 0.670652 0.502462 0.073224 0 0 0 1 0.377241 0.962914 0 0 1 0 1 0 1 0 1 0 0 1 0.976041 0 0 1 0.429796 0.000000 0 0.000000
190389 0.683712 0.622922 0.502462 0.198422 0 0 0 1 0.420731 0.984912 0 0 1 0 1 0 1 0 0 0 0 1 0.981914 0 0 1 0.114722 0.000000 0 0.000000
214556 0.512573 0.706205 0.502462 0.179820 0 0 0 1 0.369321 0.911110 0 0 1 0 1 0 1 0 0 0 0 1 0.958905 0 0 1 0.254009 0.194959 0 0.215338
254298 0.553098 0.581484 0.502462 0.240981 1 0 0 1 0.871335 0.994705 0 0 0 0 1 0 1 0 0 0 0 1 0.931391 0 0 1 0.429796 0.000000 0 0.000000
79111 0.596197 0.694093 0.724405 0.204228 0 0 0 1 0.305683 0.943627 0 1 1 1 1 0 1 0 0 0 0 0 0.999762 0 0 0 0.282797 0.000000 0 0.000000
21596 0.741115 0.342529 0.608261 0.590079 1 1 0 1 0.430596 0.966136 0 0 0 0 0 1 0 1 0 0 0 1 0.990838 0 0 1 0.107023 0.000000 0 0.000000
210443 0.726657 0.766234 0.502462 0.280101 0 0 1 0 0.783382 0.985950 0 0 1 0 1 0 1 0 1 0 0 0 0.979731 0 0 1 0.158035 0.000000 0 0.000000
6660 0.429391 0.629674 0.796800 0.331060 0 1 0 1 0.595526 0.946450 1 1 1 1 0 1 0 1 1 0 0 0 0.944497 0 0 0 0.248124 0.000000 1 0.000000
259398 0.116365 0.511034 0.534066 0.545941 0 0 0 1 0.446297 0.882231 1 0 1 0 0 1 0 1 1 0 0 1 0.962151 0 0 1 0.134897 0.000000 1 0.000000
96567 0.702764 0.683269 0.627016 0.447745 0 0 0 1 0.884535 0.998458 0 0 1 0 0 1 0 1 1 0 0 1 0.998309 0 0 1 0.268617 0.000000 0 0.000000
253843 0.665314 0.583238 0.502462 0.212740 0 0 0 1 0.377518 0.914231 0 0 1 0 1 0 1 0 1 0 0 0 0.913300 0 0 0 0.081932 0.000000 0 0.000000
289555 0.418343 0.511034 0.107190 0.861387 0 0 1 0 0.712380 1.000000 1 0 1 0 0 1 0 1 1 1 0 0 0.976100 0 0 0 0.093190 0.194959 0 0.215338
271248 0.223191 0.486653 0.108520 0.884611 0 1 1 0 0.891205 1.000000 1 1 1 1 0 1 0 1 1 1 1 1 0.995445 0 0 1 0.276330 0.000000 1 0.000000
232677 0.367178 0.253963 0.502462 0.542728 0 1 0 1 0.854245 1.000000 0 0 1 0 0 1 0 1 1 0 1 1 0.954617 0 0 1 0.097483 0.000000 0 0.000000
253321 0.733410 0.241861 0.502462 0.318489 0 1 0 1 0.639016 0.997865 1 1 1 1 0 1 0 1 1 1 0 0 0.999783 0 0 0 0.038481 0.000000 0 0.000000
95151 0.701566 0.709189 0.502462 0.501804 0 0 0 1 0.366820 0.966542 0 0 1 0 0 1 0 1 1 0 1 1 0.981013 0 0 1 0.336758 0.000000 0 0.000000
96812 0.163803 0.777659 0.502462 0.017587 0 0 0 1 0.423510 0.999916 0 1 1 1 1 0 1 0 0 0 0 0 0.986437 0 0 0 0.090864 0.000000 0 0.000000
168398 0.174921 0.710674 0.502462 0.326888 0 0 0 1 0.606225 1.000000 0 0 1 0 1 0 1 0 1 0 0 0 0.952791 0 0 0 0.491595 0.000000 0 0.000000
295235 0.653642 0.511034 0.502462 0.160823 1 0 0 1 0.369182 0.993799 0 0 0 0 1 0 1 0 0 1 0 0 0.931035 0 0 0 0.260475 0.309003 0 0.000000
118099 0.768710 0.588488 0.502462 0.345490 1 0 1 0 0.631513 1.000000 1 0 0 0 0 1 0 1 0 0 0 0 0.990442 0 0 0 0.063128 0.000000 0 0.000000
10180 0.642993 0.510090 0.502462 0.662965 0 1 0 1 0.788106 0.985228 1 0 1 0 0 1 0 1 1 1 1 0 0.978073 0 0 0 0.115414 0.000000 0 0.000000
216298 0.437591 0.384207 0.502462 0.390699 0 0 0 1 0.745588 0.969417 0 0 1 0 0 1 0 1 1 0 0 1 0.973181 1 1 1 0.635991 0.000000 0 0.000000
290928 0.485049 0.865896 0.502462 0.767136 0 1 1 0 0.913436 1.000000 0 0 1 0 0 1 0 1 1 0 0 0 0.982891 0 0 0 0.087776 0.000000 0 0.000000
77472 0.234437 0.403142 0.673917 0.629594 1 1 1 0 0.910518 0.982196 0 0 0 0 0 1 0 1 0 0 1 1 0.987256 0 0 1 0.309272 0.000000 0 0.000000
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
243548 0.589049 0.511034 0.502462 0.452593 0 1 1 0 0.899125 0.990257 0 0 1 0 0 1 0 1 1 0 1 1 0.946371 0 0 1 0.066382 0.000000 0 0.000000
133272 0.771258 0.511034 0.502462 0.052706 0 0 1 0 0.316104 1.000000 0 0 1 0 1 0 1 0 0 0 0 0 0.917359 1 1 0 0.635991 0.000000 0 0.000000
213090 0.245350 0.424130 0.269910 0.685682 0 0 1 0 0.997082 0.948309 0 0 1 0 0 1 0 1 1 0 0 0 0.964003 0 0 0 0.093190 0.000000 0 0.000000
300504 0.671271 0.631355 0.502462 0.076325 0 0 0 1 0.393775 0.938755 0 0 1 0 1 0 1 0 0 0 0 1 0.939014 1 1 1 0.157398 0.000000 0 0.000000
133629 0.588520 0.554947 0.502462 0.111725 0 0 0 1 0.322912 0.971732 0 0 1 0 0 1 0 1 1 0 0 1 0.942564 0 0 1 0.392880 0.000000 0 0.000000
179426 0.455125 0.553165 0.502462 0.058568 0 0 0 1 0.367792 0.960458 0 0 1 0 1 0 1 0 0 0 0 0 0.936417 0 0 0 0.226010 0.000000 0 0.000000
184423 0.585190 0.511034 0.502462 0.684949 0 1 1 0 0.392108 0.952856 1 0 1 0 0 1 0 1 1 0 1 1 0.999702 0 0 0 0.491595 0.000000 1 0.000000
267683 0.448247 0.771362 0.540896 0.740135 1 0 1 0 0.504933 1.000000 0 0 0 0 0 1 0 1 0 0 0 1 0.983595 0 0 1 0.260475 0.000000 0 0.000000
32711 0.720746 0.704706 0.353358 0.751973 1 0 0 1 0.895790 0.978820 0 0 0 0 0 1 0 1 1 0 0 1 0.987053 0 0 1 0.257000 0.389918 0 0.341303
143946 0.755797 0.659406 0.502462 0.852762 0 1 0 1 0.808392 0.976978 0 0 1 0 0 1 0 1 1 0 0 1 0.978078 1 1 1 1.000000 0.309003 0 0.341303
274327 0.606508 0.687933 0.502462 0.325648 1 0 0 1 0.589134 0.982841 0 1 0 1 0 1 0 1 1 0 0 1 0.976569 0 0 1 0.024260 0.000000 0 0.000000
6295 0.733201 0.593718 0.502462 0.246843 1 0 1 0 0.404057 0.905718 0 0 0 0 1 0 1 0 0 0 0 0 0.995258 0 0 0 0.257000 0.194959 0 0.215338
4499 0.667380 0.665855 0.502462 0.634498 1 1 1 0 0.413922 0.946668 1 0 0 0 0 1 0 1 1 0 0 0 0.960768 0 0 0 0.491595 0.194959 1 0.215338
66690 0.637841 0.321735 0.502462 0.107272 1 0 0 1 0.420731 0.992205 0 0 0 0 0 1 0 1 0 0 0 1 0.979773 1 1 1 0.157398 0.000000 0 0.000000
15151 0.599761 0.511034 0.508859 0.596731 0 1 0 1 0.426983 0.976876 1 1 1 1 0 1 0 1 1 0 0 1 0.991539 0 0 1 0.039215 0.000000 1 0.000000
173714 0.014522 0.207964 0.131686 0.880383 0 1 0 1 0.682784 0.994996 1 0 1 0 0 1 0 1 1 1 1 0 0.995258 0 0 0 0.114722 0.000000 0 0.000000
269544 0.344191 0.595456 0.502462 0.677847 0 1 0 1 0.232597 0.999972 0 0 1 0 0 1 0 1 1 0 0 0 0.998667 0 0 0 0.197042 0.194959 0 0.215338
174088 0.650785 0.119119 0.502462 0.287937 0 1 0 1 0.884813 0.974659 0 0 1 0 0 1 0 1 0 0 0 0 0.965498 1 1 0 0.157398 0.194959 0 0.215338
192506 0.469796 0.619528 0.729559 0.199887 0 0 0 1 0.409476 0.993623 0 0 1 0 1 0 1 0 0 0 0 0 0.998950 0 0 0 0.336758 0.000000 0 0.000000
291999 0.687741 0.424130 0.190005 0.735006 0 1 1 0 0.404613 0.872929 1 0 1 0 0 1 0 1 1 1 0 1 0.980931 0 0 1 0.254009 0.000000 1 0.000000
117796 0.705452 0.602386 0.556787 0.328918 0 1 0 1 0.674309 0.941466 1 0 1 0 0 1 0 1 0 0 0 0 0.942488 0 0 0 0.361433 0.000000 1 0.000000
283076 0.589552 0.105473 0.502462 0.703608 1 0 0 1 0.942059 0.988920 0 0 0 0 0 1 0 1 1 0 0 1 0.984378 1 1 1 1.000000 0.000000 0 0.000000
162688 0.357282 0.524496 0.502462 0.353044 0 1 0 1 0.653745 0.933286 1 0 1 0 0 1 0 1 1 0 1 0 0.980998 0 0 0 0.491595 0.000000 1 0.000000
133121 0.341123 0.083917 0.502462 0.361781 0 1 0 1 0.679450 0.920782 1 0 1 0 0 1 0 1 1 1 1 0 0.973236 0 0 0 0.197042 0.389918 0 0.341303
147443 0.160405 0.275000 0.379340 0.591263 0 0 0 1 0.321801 0.982422 0 0 1 0 0 1 0 1 1 0 0 0 0.991977 0 0 0 0.344429 0.000000 0 0.000000
107512 0.775181 0.586740 0.502462 0.089628 0 0 0 1 0.290260 0.980176 0 0 1 0 1 0 1 0 0 0 0 1 0.927569 0 0 1 0.125232 0.000000 0 0.000000
120975 0.564064 0.656158 0.610182 0.249831 0 0 0 1 0.409615 0.958465 0 0 1 0 1 0 1 0 1 0 0 1 0.940716 0 0 1 0.429796 0.309003 0 0.215338
147718 0.371186 0.633032 0.777601 0.534273 1 0 0 1 0.406697 0.993887 0 0 0 0 0 1 0 1 1 0 0 1 0.983999 0 0 1 0.311695 0.000000 0 0.000000
30306 0.491196 0.510090 0.502462 0.477452 0 0 1 0 0.956232 0.874927 0 0 1 0 0 1 0 1 1 0 0 1 0.970624 0 0 1 0.268617 0.000000 0 0.000000
85981 0.703568 0.612704 0.502462 0.222492 0 0 0 1 0.603585 1.000000 0 1 1 1 1 0 1 0 1 0 0 0 0.977877 0 0 0 0.142153 0.000000 0 0.000000
284062 0.577937 0.331251 0.400087 0.725141 1 0 0 1 0.380019 0.923506 0 0 0 0 0 1 0 1 1 0 0 1 0.973025 0 0 1 0.257000 0.000000 0 0.000000
47254 0.328144 0.612704 0.653159 0.243292 0 1 1 0 0.591080 0.941148 0 0 1 0 0 1 0 1 1 0 0 0 0.977313 0 0 0 0.311695 0.194959 0 0.215338
301528 0.702097 0.431192 0.502462 0.243517 1 0 1 0 0.378491 0.978487 1 0 0 0 0 1 0 1 1 0 0 0 0.980700 0 0 0 0.097483 0.000000 1 0.000000
8155 0.749947 0.638044 0.502462 0.149380 1 0 0 1 0.810893 0.963065 0 0 0 0 0 1 0 1 1 0 0 1 0.993683 1 1 1 1.000000 0.000000 0 0.000000
132874 0.272695 0.120641 0.502462 0.942052 0 1 1 0 0.833403 0.999889 0 1 1 1 0 1 0 1 1 0 1 1 0.989503 0 0 1 0.090864 0.000000 0 0.000000
299648 0.241355 0.105473 0.408043 0.218489 0 0 1 0 0.330277 1.000000 0 1 1 1 1 0 1 0 0 0 0 0 0.977190 0 0 0 0.097136 0.000000 0 0.000000
273255 0.727733 0.445396 0.553543 0.627565 1 1 0 1 0.358761 0.983034 0 0 0 0 0 1 0 1 1 0 0 0 0.998572 0 0 0 0.361433 0.000000 0 0.000000
219686 0.697987 0.329655 0.832507 0.418433 1 0 0 1 0.812283 0.967496 0 1 0 1 0 1 0 1 0 0 0 1 0.970579 0 0 1 0.248124 0.194959 0 0.215338
71295 0.533643 0.196334 0.502462 0.282469 1 1 1 0 0.497846 0.856538 0 0 0 0 0 1 0 1 1 0 1 1 0.940778 0 0 1 0.134537 0.000000 0 0.000000
84896 0.571866 0.607557 0.901461 0.208230 0 1 0 1 0.328053 0.994092 0 0 1 0 0 1 0 1 1 0 0 0 0.965106 0 0 0 0.093190 0.000000 0 0.000000
214835 0.726118 0.511034 0.912721 0.191770 0 0 0 1 0.387800 0.968949 0 0 1 0 1 0 1 0 1 0 0 0 0.937855 0 0 0 0.361433 0.000000 0 0.000000
133983 0.538869 0.540654 0.502462 0.642108 0 1 0 1 0.406697 0.999944 0 0 1 0 0 1 0 1 1 0 0 0 0.973923 0 0 0 0.256321 0.000000 0 0.000000
142483 0.756684 0.730987 0.732768 0.802480 1 0 0 1 0.683201 0.937069 1 0 0 0 0 1 0 1 1 1 0 0 0.982371 0 0 0 0.361433 0.194959 0 0.215338
237714 0.732936 0.294083 0.502462 0.898478 0 1 1 0 0.727109 0.991160 0 0 1 0 0 1 0 1 1 0 1 0 0.987944 0 0 1 0.134537 0.000000 0 0.000000
226814 0.670760 0.773896 0.467801 0.605975 1 1 1 0 0.977630 0.959368 0 0 0 0 0 1 0 1 1 0 0 1 0.954604 0 0 1 0.429796 0.000000 0 0.000000
34754 0.557375 0.477649 0.502462 0.714374 0 0 1 0 0.433931 0.941466 0 0 1 0 0 1 0 1 0 0 1 1 0.968479 0 0 1 0.268617 0.000000 0 0.000000
112547 0.539038 0.404878 0.502462 0.433822 1 0 1 0 0.871057 0.943895 0 0 0 0 0 1 0 1 0 0 0 1 0.996418 0 0 1 0.309272 0.309003 0 0.000000
133883 0.508316 0.336062 0.247742 0.696900 0 1 0 1 0.336946 0.937868 0 1 1 1 0 1 0 1 1 0 1 0 0.981085 0 0 0 0.248124 0.000000 0 0.000000
15708 0.549841 0.511034 0.300443 0.554566 1 1 0 1 0.858552 0.937164 0 0 0 1 0 1 0 1 0 0 0 1 0.959366 0 0 1 0.282797 0.000000 0 0.000000
51663 0.644662 0.673830 0.502462 0.822717 0 1 0 1 0.697791 0.963520 0 0 1 0 0 1 0 1 1 0 0 0 0.990311 0 0 0 0.125232 0.000000 0 0.000000
86202 0.503277 0.657784 0.502462 0.297802 0 1 0 1 0.523274 0.999861 0 0 1 0 0 1 0 1 1 0 1 0 0.995760 0 0 0 0.048783 0.000000 0 0.000000
22671 0.160288 0.389339 0.502462 0.332694 0 1 1 0 0.603029 0.992412 0 1 1 1 0 1 0 1 1 0 0 0 0.977276 0 0 0 0.090864 0.194959 0 0.215338
110078 0.057349 0.221335 0.772319 0.548591 0 1 0 1 0.431986 0.999162 0 0 1 0 0 1 0 1 1 0 0 0 0.963924 0 0 0 0.206749 0.000000 0 0.000000
34698 0.177272 0.528093 0.502462 0.725592 0 0 1 0 0.390163 0.906877 0 0 1 0 0 1 0 1 0 0 1 1 0.994130 0 0 1 0.392880 0.000000 0 0.000000
89045 0.528721 0.329655 0.494142 0.317926 0 1 0 1 0.924135 0.931076 0 0 1 0 0 1 0 1 1 0 0 1 0.930737 0 0 1 0.206749 0.000000 0 0.000000
106081 0.314709 0.652897 0.502462 0.114431 0 0 0 1 0.372516 0.966946 0 0 1 0 1 0 1 0 1 0 0 1 0.937680 0 0 1 0.097483 0.194959 0 0.215338
168229 0.632307 0.511034 0.589663 0.522661 0 1 0 1 0.403085 0.957515 1 1 1 1 0 1 0 1 1 0 0 0 0.976467 0 0 0 0.282797 0.000000 1 0.000000
287486 0.746126 0.629674 0.333642 0.667644 0 1 1 0 0.455190 0.989924 0 0 0 0 0 1 0 1 0 0 1 1 0.962597 1 1 1 1.000000 0.000000 0 0.000000
109751 0.029899 0.504681 0.502462 0.521646 1 1 1 0 0.383910 1.000000 0 0 0 0 0 1 0 1 1 0 0 1 0.943909 0 0 1 0.256321 0.000000 0 0.000000
98506 0.274185 0.511034 0.502462 0.195265 0 0 0 1 0.310268 0.994033 0 0 1 0 1 0 1 0 0 0 0 1 0.963710 0 0 1 0.361433 0.000000 0 0.000000
183734 0.171181 0.622922 0.502462 0.117080 1 0 1 0 0.415590 0.977113 0 1 0 1 1 0 1 0 1 0 0 0 0.938684 0 0 0 0.142153 0.000000 0 0.000000
70467 0.771659 0.772631 0.798818 0.421533 1 0 0 1 0.824371 0.952152 1 0 0 0 0 1 0 1 1 1 0 0 0.935047 0 0 0 0.257000 0.000000 0 0.000000
194806 0.288153 0.562060 0.626181 0.149605 0 0 0 1 0.360706 0.985480 0 0 1 0 1 0 1 0 0 0 0 0 0.950226 0 0 0 0.491595 0.000000 0 0.000000
157381 0.098745 0.806149 0.502462 0.146167 0 0 1 0 0.311797 0.980994 0 1 1 1 1 0 1 0 0 0 0 1 0.981255 0 0 1 0.276330 0.194959 0 0.215338
235167 0.637159 0.511034 0.447376 0.224126 0 0 1 0 0.797693 0.944959 0 1 1 1 1 0 1 0 1 0 0 1 0.968328 0 0 1 0.245631 0.000000 0 0.000000
125657 0.302486 0.511034 0.502462 0.806370 1 0 0 1 0.571905 0.943091 0 0 0 0 0 1 0 1 1 0 0 0 0.992500 0 0 0 0.254009 0.000000 0 0.000000
218164 0.458381 0.077499 0.321505 0.816234 0 1 0 1 0.712519 0.981158 1 0 1 0 0 1 0 1 1 1 0 1 0.978479 0 0 1 0.088357 0.000000 0 0.000000
177789 0.491010 0.754406 0.502462 0.381849 0 1 1 0 0.721273 1.000000 0 0 1 0 0 1 0 1 1 0 0 0 0.983740 0 0 0 0.491595 0.000000 0 0.000000
257426 0.468805 0.700184 0.502462 0.713303 0 0 0 1 0.965263 0.935980 0 0 1 0 0 1 0 1 0 0 0 0 0.989786 1 0 0 0.134537 0.000000 0 0.000000
269536 0.479185 0.506484 0.502462 0.140361 0 0 0 1 0.275948 0.996496 0 0 1 0 1 0 1 0 0 0 0 1 0.993357 0 0 1 0.429796 0.000000 0 0.000000
112859 0.671961 0.090815 0.502462 0.510316 0 1 1 0 0.361262 0.999889 1 0 1 0 0 1 0 1 0 0 1 0 0.983032 0 0 0 0.392880 0.000000 1 0.000000
225281 0.568849 0.511034 0.343132 0.806708 1 0 0 1 0.500347 0.957634 1 0 0 0 0 1 0 1 1 0 0 0 0.999130 0 0 0 0.421848 0.000000 1 0.000000
288880 0.621264 0.746300 0.502462 0.333596 0 1 1 0 0.603724 0.976028 1 0 1 0 0 1 0 1 0 0 0 0 0.977206 0 0 0 0.143358 0.000000 1 0.000000
246458 0.456562 0.746300 0.502462 0.116347 0 0 0 1 0.346533 1.000000 0 1 1 1 1 0 1 0 1 0 0 1 0.979512 0 0 1 0.141377 0.000000 0 0.000000
93264 0.660353 0.820383 0.688413 0.216404 0 0 0 1 0.655829 0.943136 0 0 1 0 0 1 0 1 1 0 0 1 0.974121 0 0 1 0.429796 0.000000 0 0.000000
256951 0.117928 0.554947 0.507374 0.657328 0 1 0 1 0.725997 0.992501 0 1 1 1 0 1 0 1 0 0 0 0 0.988020 0 0 0 0.276330 0.000000 0 0.000000
216688 0.273267 0.440058 0.502462 0.195434 0 0 0 1 0.305127 0.965914 0 0 1 0 1 0 1 0 0 0 0 0 0.947385 0 0 0 0.491595 0.000000 0 0.000000
67172 0.673992 0.738020 0.371270 0.744701 0 0 0 1 0.409615 0.987784 0 0 1 0 0 1 0 1 1 0 0 0 0.989748 0 0 0 0.344429 0.000000 0 0.000000
241025 0.540776 0.420611 0.659305 0.430778 0 1 0 1 0.843129 0.903928 0 0 1 0 0 1 0 1 0 0 0 1 0.980291 0 0 1 0.344429 0.309003 0 0.341303
65318 0.586368 0.706205 0.502462 0.160147 0 1 1 0 0.362790 0.999916 0 0 1 0 0 1 0 1 0 0 1 1 0.920128 0 0 1 0.254009 0.000000 0 0.000000
214283 0.560744 0.377404 0.502462 0.518884 0 1 0 1 0.945394 0.990075 1 0 1 0 0 1 0 1 1 0 0 0 0.977515 0 0 0 0.094575 0.000000 1 0.000000
193075 0.689921 0.511034 0.118002 0.948309 0 1 1 0 0.871613 0.973344 0 0 1 0 0 1 0 1 1 0 1 0 0.987718 0 0 0 0.392880 0.000000 0 0.000000
39353 0.388762 0.785052 0.502462 0.648140 0 1 0 1 0.466444 0.933383 0 1 1 1 0 1 0 1 1 0 0 0 0.984838 0 0 0 0.090864 0.000000 0 0.000000
118451 0.469425 0.511034 0.502462 0.124690 1 0 0 1 0.301931 0.994966 0 0 0 0 1 0 1 0 0 0 0 1 0.975998 0 0 1 0.206749 0.309003 0 0.341303
68840 0.606132 0.546023 0.502462 0.186979 0 0 0 1 0.427122 0.937962 0 0 1 0 1 0 1 0 0 0 0 1 0.998632 1 1 1 1.000000 0.025339 0 0.019978
61087 0.055869 0.595456 0.502462 0.437317 0 1 0 1 0.859247 0.975551 0 0 1 1 0 1 0 1 1 0 0 0 0.996475 0 0 0 0.245631 0.000000 0 0.000000
165838 0.504621 0.292588 0.502462 0.559752 0 1 0 1 0.794081 0.947664 1 0 1 0 0 1 0 1 0 1 0 1 0.959952 0 0 0 0.268617 0.000000 0 0.000000
13545 0.698927 0.634706 0.699513 0.335344 0 0 0 1 0.634987 0.987784 1 0 1 0 0 1 0 1 1 0 0 1 0.945177 1 1 1 0.446855 0.000000 1 0.000000
228576 0.514385 0.497469 0.502462 0.491094 1 1 1 0 0.990691 0.982035 1 0 0 0 0 1 0 1 0 0 0 1 0.991820 0 0 1 0.082293 0.000000 1 0.000000
265892 0.634282 0.265049 0.471588 0.789346 0 1 0 1 0.535362 0.969057 0 0 0 0 0 1 0 1 1 0 0 1 0.973136 0 0 1 0.088357 0.000000 0 0.000000
225928 0.474761 0.511034 0.502462 0.094983 0 0 0 1 0.341392 0.990528 0 0 1 1 1 0 1 0 0 0 0 1 0.985356 0 0 1 0.282797 0.000000 0 0.000000
136602 0.103586 0.445396 0.502462 0.280496 0 0 0 1 0.475754 0.990045 0 0 1 0 1 0 1 0 1 0 0 1 0.950513 0 0 1 0.392880 0.194959 0 0.215338
21959 0.635504 0.683269 0.892529 0.177170 0 0 0 1 0.398638 0.988399 0 0 1 0 1 0 1 0 1 0 0 1 0.989021 0 0 1 0.392880 0.000000 0 0.000000
55016 0.433172 0.408359 0.762635 0.282018 0 1 0 1 0.735167 0.954252 1 0 1 0 0 1 0 1 0 0 0 0 0.975287 0 0 0 0.206749 0.000000 1 0.000000
127948 0.109211 0.511034 0.502462 0.083145 0 0 0 1 0.376963 0.940508 0 0 1 1 1 0 1 0 0 0 0 0 0.950729 0 0 1 0.245631 0.000000 0 0.000000
92787 0.400122 0.511034 0.502462 0.191375 0 0 0 1 0.973600 0.994996 0 1 1 1 1 0 1 0 1 0 0 0 0.998856 0 0 0 0.245631 0.000000 0 0.000000
137848 0.729213 0.522697 0.502462 0.116122 0 0 1 0 0.384605 1.000000 0 0 1 0 1 0 1 0 0 0 0 0 0.999936 0 0 0 0.197042 0.000000 0 0.000000
128376 0.777149 0.511034 0.502462 0.245096 0 0 1 0 0.412394 0.921321 0 0 1 0 1 0 1 0 1 0 0 0 0.979413 0 0 0 0.309272 0.000000 0 0.000000
161152 0.139769 0.408359 0.502462 0.346110 0 1 0 1 0.632347 0.940645 0 0 1 0 0 1 0 1 1 0 0 1 0.989437 0 0 1 0.115414 0.000000 0 0.000000
285920 0.676256 0.481249 0.502462 0.359132 1 0 0 1 0.667917 0.953843 0 1 0 1 1 0 1 0 1 1 0 0 0.973069 0 0 0 0.090864 0.000000 0 0.000000
44262 0.415143 0.567379 0.245165 0.702255 0 0 0 1 0.487425 0.999916 1 1 1 1 0 1 0 1 1 0 1 0 0.984947 0 0 0 0.276330 0.000000 1 0.000000
75766 0.444383 0.744932 0.502462 0.839741 0 1 1 0 0.587884 0.892583 0 0 1 0 0 1 0 1 1 0 0 1 0.979892 0 0 1 0.392880 0.000000 0 0.000000
288249 0.181751 0.265049 0.284552 0.951127 0 0 0 1 0.860636 0.991519 0 0 1 0 0 1 0 1 1 0 1 1 0.980421 1 1 1 0.157398 0.000000 0 0.000000
18047 0.489108 0.579727 0.502462 0.216855 0 1 1 0 0.453661 0.986606 0 0 1 0 0 1 0 1 0 0 1 0 0.988517 0 0 0 0.254009 0.000000 0 0.000000
152906 0.769040 0.511034 0.777013 0.169617 0 0 0 1 0.350841 0.972014 0 0 1 0 1 0 1 0 0 0 0 1 0.945552 0 0 1 0.030685 0.000000 0 0.000000
25939 0.341413 0.384207 0.502462 0.653608 0 1 1 0 0.389468 0.937352 1 0 1 0 0 1 0 1 1 0 1 1 0.982730 0 0 1 0.392880 0.000000 1 0.000000
200235 0.514385 0.723837 0.502462 0.516460 0 1 0 1 0.415034 0.936455 0 0 0 0 0 1 0 1 1 0 1 0 0.949664 0 0 0 0.336758 0.000000 0 0.000000
243307 0.179895 0.703203 0.502462 0.647520 0 1 0 1 0.984716 0.942326 1 0 1 0 0 1 0 1 1 1 0 0 0.980957 0 0 1 0.429796 0.194959 1 0.215338
2693 0.565478 0.746300 0.839738 0.092672 0 1 0 1 0.379186 0.972681 0 0 1 0 0 1 0 1 1 0 0 1 0.995196 0 0 1 0.107023 0.000000 0 0.000000
109556 0.012572 0.511034 0.502462 0.914262 0 1 1 0 0.771710 0.989712 0 1 1 1 0 1 0 1 1 0 0 1 0.990507 0 0 0 0.025534 0.000000 0 0.000000
64044 0.603079 0.382502 0.583951 0.189290 0 0 0 1 0.356398 0.927963 0 0 1 0 1 0 1 0 1 0 0 0 0.999417 0 0 0 0.093190 0.000000 0 0.000000
43585 0.711343 0.683269 0.502462 0.264036 1 1 0 1 0.435737 0.997410 0 0 0 0 0 1 0 1 0 0 0 1 0.912269 1 1 1 0.446855 0.000000 0 0.000000
164899 0.513797 0.712155 0.502462 0.668771 0 1 1 0 0.494373 0.911869 0 0 1 0 0 1 0 1 1 0 0 0 0.975835 0 0 0 0.268617 0.000000 0 0.000000
250396 0.680159 0.662638 0.502462 0.139741 0 0 0 1 0.414478 0.996065 0 0 1 0 1 0 1 0 1 0 0 1 0.937886 0 0 0 0.254009 0.309003 0 0.341303
249002 0.645834 0.384207 0.502462 0.539008 0 1 0 1 0.408365 0.985480 1 0 1 0 0 1 0 1 1 1 1 0 0.961409 0 0 0 0.268617 0.000000 0 0.000000
198286 0.668607 0.417100 0.502462 0.649718 0 1 0 1 0.423510 1.000000 0 0 1 0 0 1 0 1 1 0 1 0 0.980632 0 0 0 0.361433 0.000000 0 0.000000
202982 0.759248 0.770087 0.731817 0.504228 1 1 1 0 0.287064 0.974349 1 0 0 0 0 1 0 1 1 1 1 1 0.995733 0 0 1 0.134897 0.000000 1 0.000000
105878 0.615184 0.687933 0.584956 0.677734 0 1 0 1 0.368765 0.966283 1 0 1 0 0 1 0 1 1 0 0 0 0.979669 0 0 0 0.344429 0.000000 1 0.000000
211428 0.585944 0.622922 0.493064 0.543574 1 1 0 1 0.263860 0.986824 1 0 0 0 0 1 0 1 0 0 0 0 0.993361 0 0 0 0.254009 0.000000 1 0.000000
110687 0.401661 0.511034 0.241867 0.939515 0 1 1 0 0.828123 0.989681 0 0 1 0 0 1 0 1 0 0 1 1 0.987164 0 0 1 0.094492 0.000000 0 0.000000
185340 0.574626 0.071055 0.502462 0.664769 0 1 0 1 0.427400 0.989347 0 0 1 0 0 1 0 1 1 0 0 0 0.973258 0 0 0 0.392880 0.000000 0 0.000000
107059 0.197120 0.657784 0.502462 0.323168 0 1 1 0 0.577185 0.938848 0 0 1 0 0 1 0 1 0 0 1 0 0.960181 0 0 0 0.429796 0.000000 0 0.000000
122096 0.492994 0.085595 0.175511 0.914092 0 0 1 0 0.771571 0.980011 0 1 1 1 0 1 0 1 0 0 1 0 0.983790 0 0 0 0.097136 0.000000 0 0.000000
248550 0.598848 0.344155 0.502462 0.678805 1 0 0 1 0.565791 0.923612 0 0 0 0 0 1 0 1 1 0 0 0 0.976494 0 0 0 0.113324 0.000000 0 0.000000
50015 0.486267 0.689479 0.715775 0.526719 1 0 0 1 0.343615 0.960845 0 0 0 0 0 1 0 1 1 0 0 1 0.992110 0 0 0 0.066382 0.000000 0 0.000000
245310 0.381615 0.574447 0.502462 0.369504 0 1 0 1 0.691260 0.982132 1 1 1 1 0 1 0 1 1 0 0 1 0.971148 0 0 1 0.282797 0.000000 1 0.000000
253618 0.556668 0.598926 0.454929 0.743461 1 0 0 1 0.491872 0.968478 0 0 0 0 0 1 0 1 1 0 0 0 0.997285 0 0 0 0.491595 0.000000 0 0.000000
23419 0.342830 0.511034 0.502462 0.865220 1 1 1 0 0.649020 0.976606 1 1 0 1 0 1 0 1 1 0 0 0 0.945603 0 0 0 0.245631 0.000000 1 0.000000
122409 0.698863 0.461482 0.335649 0.702029 1 0 1 0 0.425316 0.999442 0 0 0 0 0 1 0 1 0 0 0 1 0.988422 0 0 0 0.114722 0.000000 0 0.000000
218969 0.582709 0.736623 0.502462 0.158061 0 0 0 1 0.379186 0.943583 0 0 1 0 1 0 1 0 0 0 0 0 0.919079 0 0 0 0.254009 0.000000 0 0.000000
218126 0.527462 0.610991 0.349502 0.617362 0 0 0 1 0.292344 0.989924 1 0 1 0 0 1 0 1 1 1 0 0 0.993130 0 0 0 0.197042 0.000000 0 0.000000
119176 0.014315 0.475850 0.603088 0.378072 0 1 0 1 0.711408 1.000000 1 0 1 0 0 1 0 1 1 0 0 0 0.980209 0 0 0 0.392880 0.194959 1 0.215338
121626 0.574717 0.733815 0.502462 0.551240 0 1 0 1 0.791858 0.975449 0 0 1 0 0 1 0 1 1 0 0 0 0.952573 0 0 0 0.196267 0.000000 0 0.000000
90982 0.714507 0.636376 0.793094 0.304340 0 1 0 1 0.532861 0.906085 0 0 1 0 0 1 0 1 1 0 0 1 0.980022 0 0 1 0.260475 0.000000 0 0.000000
98806 0.033521 0.511034 0.502462 0.205919 0 0 0 1 0.776435 1.000000 0 1 1 1 1 0 1 0 0 0 0 0 0.991117 0 0 0 0.276330 0.309003 0 0.341303
214020 0.615986 0.429424 0.502462 0.678072 1 1 1 0 0.403363 0.975106 0 0 0 0 0 1 0 1 1 0 1 1 0.962560 0 0 1 0.336758 0.000000 0 0.000000
184064 0.658335 0.511034 0.502462 0.151578 0 0 1 0 0.397944 0.956357 0 0 1 0 1 0 1 0 0 0 0 1 0.975391 0 0 1 0.113324 0.000000 0 0.000000
158338 0.471986 0.669057 0.733521 0.434555 1 0 1 0 0.858691 0.990709 0 0 0 0 0 1 0 1 0 0 0 1 0.995676 0 0 1 0.392880 0.194959 0 0.000000
139182 0.212466 0.681706 0.502462 0.311218 0 0 0 1 0.557038 1.000000 0 0 1 0 1 0 1 0 0 0 0 1 0.964859 0 0 1 0.336758 0.000000 0 0.000000
201664 0.709494 0.095070 0.250386 0.833822 0 1 1 0 0.582604 0.860985 0 0 0 0 0 1 0 1 1 0 0 1 0.993701 1 1 1 0.157398 0.000000 0 0.000000
24538 0.673093 0.755740 0.466927 0.532525 0 0 1 0 0.682923 0.982873 1 0 1 0 0 1 0 1 1 0 1 1 0.974380 0 0 1 0.134537 0.000000 1 0.000000
273109 0.288122 0.761026 0.502462 0.489966 0 1 0 1 0.993331 1.000000 1 1 1 1 0 1 0 1 1 0 1 0 0.973119 0 0 0 0.276330 0.000000 1 0.000000
123684 0.364221 0.483050 0.387094 0.566460 0 1 0 1 0.336529 0.942100 0 0 1 0 0 1 0 1 1 0 0 0 0.986544 0 0 0 0.093190 0.000000 0 0.000000
77373 0.726827 0.372334 0.502462 0.398027 0 1 1 0 0.761845 0.979945 0 0 1 0 0 1 0 1 1 0 0 1 0.976504 1 1 1 0.157398 0.000000 0 0.000000
297366 0.499029 0.510090 0.502462 0.868320 0 0 1 0 0.684591 0.986513 1 1 1 1 0 1 0 1 1 1 0 0 0.982543 0 0 0 0.282797 0.000000 0 0.000000
30535 0.358840 0.617826 0.708992 0.374126 0 1 0 1 0.701125 0.993270 1 0 1 0 0 1 0 1 1 0 0 0 0.960149 0 0 0 0.257000 0.194959 1 0.215338
270536 0.208819 0.511034 0.502462 0.543968 0 1 1 0 0.404057 1.000000 1 0 1 0 0 1 0 1 1 0 0 1 0.983429 0 0 1 0.206749 0.000000 1 0.000000
284806 0.651902 0.570917 0.502462 0.201409 1 0 0 1 0.371127 0.943493 0 0 0 0 0 1 0 1 1 0 0 0 0.931188 0 0 0 0.125232 0.000000 0 0.000000
183323 0.565757 0.211551 0.266392 0.894645 0 0 0 1 0.857718 0.974107 0 0 1 0 0 1 0 1 1 0 0 1 0.999126 0 0 1 0.196267 0.000000 0 0.000000
104488 0.600931 0.511034 0.502462 0.743236 0 1 0 1 0.873142 0.981970 0 0 1 0 0 1 0 1 1 0 0 1 0.960617 0 0 1 0.138276 0.000000 0 0.000000
288998 0.593019 0.275000 0.502462 0.225197 0 1 0 1 0.344588 0.951276 0 0 1 0 0 1 0 1 1 0 0 1 0.961846 0 0 1 0.196267 0.000000 0 0.000000
134633 0.606148 0.807274 0.502462 0.113134 1 1 0 1 0.372655 0.953638 0 0 0 0 0 1 0 1 1 0 0 1 0.928322 0 0 1 0.143358 0.000000 0 0.000000
300804 0.633206 0.457900 0.869146 0.630665 1 0 0 1 0.253439 0.983642 1 0 0 0 0 1 0 1 1 0 0 0 0.983038 0 0 0 0.491595 0.000000 1 0.000000
12666 0.521583 0.349055 0.502462 0.232356 1 0 0 1 0.412116 1.000000 0 0 0 0 1 0 1 0 1 0 0 1 0.995338 0 0 1 0.134537 0.000000 0 0.000000
129312 0.383113 0.511034 0.502462 0.508568 0 1 0 1 0.339864 0.991609 0 1 1 1 0 1 0 1 1 0 0 0 0.977499 0 0 0 0.090864 0.000000 0 0.000000
61858 0.578225 0.387625 0.809070 0.485851 0 1 0 1 0.979436 0.937069 0 0 1 0 0 1 0 1 1 0 1 1 0.968415 0 0 1 0.226010 0.000000 0 0.000000
13986 0.627382 0.782608 0.502462 0.653100 0 0 0 1 0.403085 1.000000 0 0 1 0 0 1 0 1 1 0 0 0 0.960516 0 0 0 0.361433 0.000000 0 0.000000
164231 0.545714 0.438281 0.662712 0.803326 0 1 0 1 0.949423 0.972260 0 0 1 0 0 1 0 1 1 0 0 0 0.976547 0 0 0 0.107023 0.000000 0 0.000000
301648 0.397228 0.251239 0.502462 0.250338 0 0 0 1 0.616229 0.996381 0 0 0 0 1 0 1 0 0 0 0 0 0.934573 0 0 0 0.206749 0.000000 0 0.000000
136330 0.384364 0.519097 0.287243 0.595039 0 1 1 0 0.967903 0.952069 0 1 1 1 0 1 0 1 0 0 0 0 0.986257 0 0 0 0.245631 0.000000 0 0.000000
179262 0.683035 0.515495 0.424607 0.553946 0 1 0 1 0.390302 0.964947 0 0 1 0 0 1 0 1 1 0 0 1 0.976294 0 0 1 0.336758 0.000000 0 0.000000
124375 0.031548 0.689479 0.502462 0.207272 0 0 0 1 0.511463 0.999358 0 1 1 1 1 0 1 0 0 0 0 0 0.978832 0 0 0 0.276330 0.000000 0 0.000000
133767 0.449626 0.294083 0.287380 0.883315 0 0 0 1 0.688759 0.992353 1 1 0 1 0 1 0 1 1 1 0 1 0.997955 0 0 1 0.245631 0.000000 0 0.000000
120151 0.507250 0.492060 0.502462 0.297238 0 1 0 1 0.514798 0.992590 1 1 1 1 0 1 0 1 1 1 1 0 0.991880 0 0 0 0.248124 0.000000 1 0.000000
264712 0.744960 0.291097 0.118813 0.865558 0 1 1 0 0.658191 0.990438 0 1 1 1 0 1 0 1 1 0 0 1 0.997998 0 0 1 0.248124 0.000000 0 0.000000
302918 0.401855 0.256706 0.721345 0.824803 0 1 0 1 0.622898 1.000000 1 0 1 0 0 1 0 1 1 0 0 0 0.980276 1 1 0 0.635991 0.194959 1 0.215338
48984 0.668050 0.506484 0.502462 0.613416 0 1 1 0 0.458108 0.981711 1 0 1 0 0 1 0 1 1 1 0 0 0.992340 0 0 0 0.094492 0.194959 0 0.215338
38044 0.753209 0.746300 0.502462 0.576888 0 1 1 0 0.304155 0.971873 0 0 1 0 0 1 0 1 1 0 0 1 0.950806 0 0 1 0.079177 0.000000 0 0.000000
236584 0.687990 0.614414 0.502462 0.154904 0 0 0 1 0.583576 0.952773 0 0 1 0 0 1 0 1 1 0 0 0 0.990395 1 1 0 0.635991 0.000000 0 0.000000
217851 0.716208 0.788681 0.502462 0.613923 0 1 1 0 0.342504 0.951025 0 0 1 0 0 1 0 1 1 0 0 0 0.957756 0 0 0 0.268617 0.000000 0 0.000000
194776 0.624597 0.362277 0.598003 0.309921 1 0 1 0 0.554120 0.932260 0 0 0 0 0 1 0 1 0 0 0 1 0.998318 0 0 1 0.138276 0.000000 0 0.000000
274329 0.609515 0.511034 0.593740 0.628016 0 0 0 1 0.323607 0.961308 0 0 1 0 0 1 0 1 1 0 0 0 0.984039 0 0 0 0.344429 0.000000 0 0.000000
9268 0.655145 0.459690 0.502462 0.265276 0 1 0 1 0.445463 0.989073 0 0 1 0 0 1 0 1 1 0 0 1 0.902871 0 0 1 0.097483 0.194959 0 0.215338
82798 0.631076 0.424130 0.502462 0.348027 0 1 0 1 0.653884 0.993916 1 0 1 0 0 1 0 1 1 1 1 0 0.957451 0 0 0 0.081932 0.000000 0 0.000000
24300 0.517101 0.252599 0.210627 0.593856 0 0 0 1 0.321801 0.973135 0 0 1 0 0 1 0 1 1 1 0 0 0.992340 0 0 0 0.344429 0.000000 1 0.000000
23247 0.601026 0.511034 0.502462 0.753382 1 0 1 0 0.456857 0.911460 0 1 0 1 0 1 0 1 1 0 1 1 0.988931 0 0 1 0.248124 0.000000 0 0.000000
220884 0.348146 0.413597 0.502462 0.563754 0 0 0 1 0.404891 0.967093 0 1 1 1 0 1 0 1 1 0 0 1 0.953071 0 0 1 0.245631 0.000000 0 0.000000
55591 0.668040 0.511034 0.397582 0.723788 0 1 0 1 0.452689 0.971662 0 0 1 0 0 1 0 1 0 0 0 0 0.973741 0 0 0 0.491595 0.194959 0 0.000000
263160 0.561508 0.829750 0.502462 0.399887 0 1 0 1 0.765180 0.905902 0 0 1 0 0 1 0 1 1 0 0 0 0.931128 0 0 0 0.254009 0.000000 0 0.000000
257750 0.636248 0.590233 0.585619 0.719391 1 0 0 1 0.988606 0.948823 0 0 0 0 0 1 0 1 1 0 0 1 0.989550 0 0 1 0.226010 0.000000 0 0.000000
40397 0.529522 0.511034 0.450636 0.871871 1 1 0 1 0.849660 0.975688 0 0 0 0 0 1 0 1 1 0 0 1 0.994332 0 0 0 0.197042 0.000000 0 0.000000
11534 0.557720 0.511034 0.561073 0.388839 0 1 0 1 0.738224 0.968985 1 0 1 0 0 1 0 1 1 0 1 1 0.981147 0 0 1 0.067213 0.000000 1 0.000000
99299 0.458348 0.399676 0.502462 0.403551 0 1 0 1 0.775462 1.000000 0 0 1 0 0 1 0 1 1 0 0 0 0.991774 0 0 0 0.392880 0.000000 0 0.000000
223165 0.183987 0.499272 0.502462 0.551804 0 0 1 0 0.446297 0.982905 0 0 1 0 0 1 0 1 0 0 0 1 0.981444 0 0 1 0.087776 0.000000 0 0.000000
37065 0.455537 0.636376 0.502462 0.132694 0 0 0 1 0.381687 0.993064 0 1 1 1 1 0 1 0 0 0 0 0 0.979157 0 0 0 0.024260 0.000000 0 0.000000
196769 0.727048 0.629674 0.668181 0.616742 0 0 0 1 0.654995 0.904176 0 0 1 0 0 1 0 1 1 0 0 1 0.955664 1 1 1 1.000000 0.194959 0 0.000000
202283 0.556776 0.511034 0.364565 0.851297 1 1 1 0 0.885925 1.000000 1 0 0 0 0 1 0 1 1 1 1 0 0.977281 0 0 0 0.256321 0.309003 0 0.000000
271836 0.268521 0.511034 0.603256 0.280496 0 0 0 1 0.473809 0.990498 0 0 1 0 1 0 1 0 1 0 0 1 0.927964 0 0 1 0.197042 0.000000 0 0.000000
171829 0.352765 0.558507 0.250749 0.571139 0 1 0 1 0.274559 0.987444 1 1 1 1 0 1 0 1 1 0 0 0 0.987492 0 0 0 0.142153 0.000000 1 0.000000
240181 0.423347 0.511034 0.564334 0.776099 0 1 0 1 0.593720 0.927300 1 1 1 1 0 1 0 1 0 0 0 0 0.985223 0 0 0 0.276330 0.000000 1 0.000000
208261 0.145880 0.511034 0.502462 0.617756 0 1 0 1 0.400028 0.976300 0 0 1 0 0 1 0 1 1 0 1 0 0.963606 0 0 0 0.109945 0.000000 0 0.000000
281601 0.627306 0.511034 0.502462 0.736753 0 0 0 1 0.344588 0.966946 0 0 1 0 0 1 0 1 1 0 0 0 0.972735 0 0 0 0.064665 0.000000 0 0.000000
200551 0.640554 0.621226 0.884205 0.271082 1 0 0 1 0.449632 0.990468 0 0 0 0 0 1 0 1 0 0 0 0 0.984740 0 0 0 0.491595 0.000000 0 0.000000
298064 0.762391 0.372334 0.710067 0.765276 0 1 0 1 0.752119 0.975414 1 0 1 0 0 1 0 1 1 1 0 0 0.977505 0 0 0 0.088357 0.000000 0 0.000000
80077 0.650904 0.789880 0.502462 0.311894 0 1 0 1 0.554676 0.943672 0 0 1 0 0 1 0 1 1 0 0 1 0.987150 0 0 1 0.309272 0.000000 0 0.000000
106530 0.632297 0.627991 0.502462 0.429030 1 1 0 1 0.838683 0.979516 0 0 0 0 0 1 0 1 0 0 0 1 0.964064 0 0 1 0.088357 0.000000 0 0.000000
279303 0.018314 0.511034 0.558322 0.113980 0 0 0 1 0.384327 1.000000 0 0 1 0 1 0 1 0 1 0 0 0 0.986228 0 0 0 0.429796 0.194959 0 0.215338
256508 0.694315 0.591977 0.502462 0.154453 0 0 1 0 0.401278 0.946624 0 0 1 0 1 0 1 0 0 0 0 0 0.964180 0 0 0 0.206749 0.000000 0 0.000000
48555 0.697642 0.698668 0.502462 0.325310 1 0 0 1 0.583299 1.000000 0 0 0 0 0 1 0 1 1 0 0 1 0.977866 0 0 1 0.491595 0.000000 0 0.000000
154555 0.669241 0.497469 0.779832 0.393743 0 1 0 1 0.753092 0.984438 0 0 0 0 0 1 0 1 1 0 0 1 0.978453 0 0 1 0.094492 0.000000 0 0.000000
251995 0.490409 0.332851 0.502462 0.421139 0 1 0 1 0.818674 0.966468 0 0 1 0 0 1 0 1 1 0 0 0 0.981679 0 0 0 0.392880 0.000000 0 0.000000
124243 0.597586 0.511034 0.502462 0.084273 1 0 1 0 0.400167 0.983514 0 0 0 0 1 0 1 0 0 0 0 1 0.954838 0 0 1 0.421848 0.000000 0 0.000000
252801 0.516795 0.493863 0.624748 0.177508 1 0 1 0 0.446992 0.974107 0 0 0 0 1 0 1 0 1 1 0 1 0.998804 0 0 1 0.136490 0.000000 0 0.000000
68148 0.615194 0.614414 0.502462 0.270688 0 0 0 1 0.448520 0.965728 0 0 1 0 0 1 0 1 1 0 0 1 0.981541 0 0 0 0.093190 0.000000 0 0.000000
207624 0.621809 0.593718 0.521853 0.311218 0 0 0 1 0.549673 0.928268 0 0 1 0 0 1 0 1 1 0 0 1 0.985862 0 0 1 0.256321 0.000000 0 0.000000
174073 0.700402 0.385915 0.502462 0.180214 0 0 0 1 0.388773 1.000000 0 0 1 0 0 1 0 1 1 0 1 0 0.964635 1 1 0 0.157398 0.000000 0 0.000000
205041 0.170701 0.401407 0.502462 0.270011 0 0 0 1 0.600250 0.934929 0 0 1 0 1 0 1 0 0 0 0 0 0.952381 0 0 0 0.268617 0.000000 0 0.000000
239629 0.667729 0.304672 0.625365 0.249887 0 0 0 1 0.396971 0.956196 0 0 1 0 1 0 1 0 0 0 0 0 0.899031 0 0 0 0.344429 0.000000 0 0.000000
239931 0.723261 0.798137 0.502462 0.503720 1 0 0 1 0.445047 0.941693 0 0 0 0 0 1 0 1 0 0 0 1 0.974242 1 1 0 0.446855 0.000000 0 0.000000
270936 0.679645 0.190706 0.502462 0.377903 0 1 0 1 0.716549 0.967861 0 0 1 0 0 1 0 1 1 0 0 0 0.941698 0 0 0 0.254009 0.000000 0 0.000000
141699 0.388689 0.843544 0.477087 0.556032 0 0 0 1 0.427956 0.978986 0 0 1 0 0 1 0 1 0 0 0 1 0.967307 0 0 0 0.093190 0.000000 0 0.000000
256840 0.718818 0.880268 0.679845 0.516460 0 0 0 1 0.279700 0.725477 0 0 1 0 0 1 0 1 1 0 1 1 0.944393 1 1 0 0.635991 0.000000 0 0.000000
41606 0.526181 0.846378 0.502462 0.131454 0 0 0 1 0.328331 0.901159 0 0 1 0 1 0 1 0 0 0 0 0 0.945133 0 0 0 0.134897 0.000000 0 0.000000
3890 0.590446 0.643026 0.502462 0.133709 0 0 0 1 0.488120 0.995604 0 0 1 0 1 0 1 0 0 0 0 0 0.919919 0 0 0 0.087776 0.000000 0 0.000000
273538 0.614464 0.569149 0.815896 0.317982 0 0 0 1 0.564124 1.000000 0 0 1 0 1 0 1 0 0 0 0 0 0.946108 0 0 1 0.064665 0.000000 0 0.000000
31551 0.472188 0.511034 0.578797 0.637768 0 0 0 1 0.302765 0.956036 0 1 1 1 0 1 0 1 1 0 0 0 0.988355 0 0 0 0.276330 0.000000 0 0.000000
67435 0.637553 0.723837 0.502462 0.155468 0 0 0 1 0.939836 0.999972 0 0 1 0 1 0 1 0 1 0 0 1 0.938069 1 1 1 0.157398 0.000000 0 0.000000
84654 0.730975 0.511034 0.502462 0.160034 0 0 0 1 0.422954 0.901159 0 0 1 0 1 0 1 0 1 0 0 0 0.943834 0 0 0 0.392880 0.000000 0 0.000000
129981 0.598779 0.452534 0.374224 0.584047 0 1 0 1 0.298736 0.971909 0 1 1 1 0 1 0 1 1 0 0 1 0.995316 0 0 1 0.282797 0.000000 0 0.000000
65725 0.608917 0.188490 0.447677 0.437655 0 0 0 1 0.865916 1.000000 1 1 1 1 0 1 0 1 1 1 0 1 0.975064 0 0 1 0.276330 0.000000 0 0.000000
123855 0.558405 0.803885 0.502462 0.476832 1 0 1 0 0.961929 0.989469 0 0 0 0 1 0 1 0 1 0 0 0 0.973164 0 0 0 0.129705 0.000000 0 0.000000
2747 0.718509 0.511034 0.502462 0.124239 0 0 0 1 0.443240 0.897868 0 0 1 0 1 0 1 0 1 0 0 1 0.942109 1 1 1 0.446855 0.000000 0 0.000000
130523 0.720829 0.502878 0.502462 0.410485 0 0 1 0 0.828679 0.927861 0 0 1 0 0 1 0 1 1 0 1 0 0.940654 0 0 0 0.491595 0.000000 0 0.000000
149503 0.590879 0.303146 0.700113 0.630609 0 1 1 0 0.938724 0.967569 0 0 1 0 0 1 0 1 1 0 1 1 0.958132 0 0 1 0.361433 0.000000 0 0.000000
156730 0.676758 0.511034 0.502462 0.199380 0 0 1 0 0.411005 0.994879 0 0 1 0 1 0 1 0 0 0 0 0 0.944207 0 0 0 0.421848 0.000000 0 0.000000
258795 0.638508 0.713631 0.703340 0.491939 0 1 0 1 0.993192 1.000000 1 0 1 0 0 1 0 1 1 0 0 0 0.977297 0 0 0 0.257000 0.000000 1 0.000000
184779 0.630077 0.267869 0.502462 0.686133 0 1 1 0 0.831735 0.973309 0 0 1 0 0 1 0 1 1 0 0 0 0.989644 0 0 0 0.128209 0.000000 0 0.000000
214176 0.688060 0.432962 0.502462 0.602311 1 1 0 1 0.389190 0.979252 0 0 0 0 0 1 0 1 1 0 0 1 0.989409 0 0 1 0.361433 0.000000 0 0.000000
235796 0.703955 0.511034 0.502462 0.404904 1 1 1 0 0.777268 0.977449 0 0 0 0 0 1 0 1 1 0 1 1 0.939358 0 0 1 0.421848 0.000000 0 0.000000
103355 0.089042 0.165407 0.502462 0.642165 0 1 1 0 0.358205 1.000000 1 0 0 0 0 1 0 1 1 1 0 1 0.960250 0 0 1 0.143358 0.000000 1 0.000000
267455 0.679955 0.718033 0.502462 0.626437 1 0 0 1 0.339169 0.966909 0 0 0 0 0 1 0 1 1 0 0 1 0.967901 0 0 1 0.254009 0.000000 0 0.000000
199041 0.644206 0.670652 0.502462 0.193010 0 0 0 1 0.408226 0.968696 0 0 0 0 1 0 1 0 1 0 0 1 0.943475 0 0 1 0.361433 0.194959 0 0.215338
252709 0.540532 0.535276 0.502462 0.226043 0 1 0 1 0.358066 0.936692 1 0 1 0 0 1 0 1 0 0 0 0 0.988083 0 0 0 0.113324 0.000000 1 0.000000
194027 0.136195 0.520898 0.502462 0.484611 0 1 1 0 0.987078 0.984311 0 0 1 0 0 1 0 1 1 0 1 1 0.953539 1 1 1 0.635991 0.000000 0 0.000000
262913 0.518139 0.349055 0.502462 0.936753 1 1 1 0 0.822287 1.000000 0 0 0 0 0 1 0 1 0 0 0 0 0.971102 0 0 0 0.134537 0.000000 0 0.000000
64820 0.738505 0.768808 0.596087 0.544701 1 1 0 1 0.578713 0.995112 0 0 0 0 0 1 0 1 0 0 0 1 0.986218 0 0 1 0.309272 0.000000 0 0.000000
41090 0.738370 0.352340 0.737286 0.309865 0 1 0 1 0.559122 0.906146 0 0 1 0 0 1 0 1 0 0 0 1 0.975835 0 0 1 0.206749 0.000000 0 0.000000
278167 0.699755 0.716570 0.460309 0.556933 0 1 0 1 0.583576 0.947707 0 0 1 0 0 1 0 1 1 0 1 1 0.966573 0 0 1 0.123030 0.000000 0 0.000000
191335 0.663158 0.173527 0.502462 0.217531 1 0 0 1 0.336946 0.980798 0 0 0 0 1 0 1 0 1 0 0 1 0.955285 0 0 1 0.206749 0.000000 0 0.000000
175203 0.265018 0.466864 0.724350 0.626494 0 0 0 1 0.654578 1.000000 0 0 1 0 0 1 0 1 1 0 0 0 0.960326 0 0 0 0.491595 0.194959 0 0.215338
87498 0.317323 0.738020 0.502462 0.363585 0 1 0 1 0.676393 1.000000 0 0 1 0 0 1 0 1 1 0 0 1 0.999152 0 0 1 0.196267 0.000000 0 0.000000
137337 0.627444 0.385915 0.470037 0.841319 1 1 0 1 0.620675 0.963103 0 0 0 0 0 1 0 1 0 0 0 0 0.966001 0 0 0 0.129705 0.000000 0 0.000000
54886 0.628664 0.546023 0.502462 0.216234 0 1 0 1 0.346394 0.919588 0 0 1 0 0 1 0 1 1 0 0 1 0.971176 0 0 0 0.197042 0.000000 0 0.000000
207892 0.446620 0.511034 0.502462 0.270913 0 0 0 1 0.658747 0.989651 0 0 1 0 1 0 1 0 1 0 0 1 0.972399 1 1 1 1.000000 0.000000 0 0.000000
110268 0.767108 0.372334 0.502462 0.519673 0 1 1 0 0.954148 0.995054 0 0 1 0 0 1 0 1 0 0 1 1 0.972713 0 0 1 0.138276 0.000000 0 0.000000
119879 0.617180 0.511034 0.502462 0.605862 0 1 0 1 0.425594 0.983162 0 1 1 1 0 1 0 1 1 0 0 0 0.982335 0 0 0 0.083289 0.000000 0 0.000000
259178 0.722398 0.370650 0.455170 0.548873 0 0 0 1 0.379603 0.965988 0 1 1 1 0 1 0 1 1 0 0 0 0.992907 0 0 0 0.038481 0.000000 0 0.000000
131932 0.608427 0.554947 0.305919 0.615558 1 1 1 0 0.288176 0.910228 0 0 0 0 0 1 0 1 0 0 0 1 0.975922 0 0 1 0.197042 0.000000 0 0.000000
146867 0.597959 0.259468 0.611373 0.705919 0 1 1 0 0.387800 1.000000 1 0 1 0 0 1 0 1 0 0 0 1 0.996003 1 0 1 0.134537 0.000000 1 0.000000
121958 0.408400 0.275000 0.351724 0.679425 1 1 1 0 0.381826 0.973205 1 0 0 0 0 1 0 1 1 1 0 0 0.986208 0 0 0 0.129331 0.000000 0 0.000000

246008 rows × 30 columns

In [639]:
# 21. Build a data preprocessing pipeline to used for all testing sets. 
# This pipeline will recreate all features that were engineered in the 
# training set during the original data preprocessing phase. 
# The pipeline will also apply the imputer, min-max, and PCA transforms 
# originally fit on features in the training set to all datapoints in a 
# testing set.

def adjust_columns_application_test_csv_table(testing_dataframe):
    """
    After it is one-hot encoded, application_test.csv data table will have one 
    extra column, 'REGION_RATING_CLIENT_W_CITY_-1', that is not present in the 
    training dataframe. This column will be removed from the testing datatable 
    in this case. Only 1 of the 48,744 rows in application_test.csv will have a 
    value of 1 for this feature following one-hot encoding. I am not worried 
    about this column's elimination from the testing dataframe affecting predictions.
    
    Additionally, unlike the test validation set, which originally comprised 20% of 
    application_train.csv, application_test.csv will be missing the following columns 
    after it is one-hot encoded:
    
    'CODE_GENDER_XNA', 'NAME_INCOME_TYPE_Maternity leave', 'NAME_FAMILY_STATUS_Unknown'
    
    In this case, we need to insert these columns into the testing dataframe, at 
    the exact same indices they are located at in the fully preprocessed training 
    dataframe.Each inserted column will be filled with all zeros. (If each of these 
    binary features are missing from the application_test.csv data table, we can infer 
    that each borrower in thatdata table obviously would have a 0 for each feature were 
    it present.)

    Parameters:
        testing_dataframe: Pandas dataframe containing the testing dataset
                           contained in the file application_test.csv
        
    Returns: a testing dataframe containing the exact same columns and 
             column order as found in the training dataframe
    """
    
    # Identify any columns in the one-hot encoded testing_dataframe that 
    # are not in X_train_raw. These columns will need to be removed from the
    # testing_dataframe. (Expected that there will only be one such 
    # column: 'REGION_RATING_CLIENT_W_CITY_-1')
    X_train_columns_list = X_train_raw.columns.tolist()
    testing_dataframe_columns_list = testing_dataframe.columns.tolist()
    for column_name in X_train_columns_list:
        if column_name in testing_dataframe_columns_list:
            testing_dataframe_columns_list.remove(column_name)
            columns_not_in_X_train_raw = testing_dataframe_columns_list
    
    # Drop any column from the testing_dataframe that is not in the 
    # training dataframe. Expected to only be the one column 'REGION_RATING_CLIENT_W_CITY_-1'
    for column in columns_not_in_X_train_raw:
        testing_dataframe = testing_dataframe.drop(column, axis=1)
    
    # Get the column indices of each of the features 'CODE_GENDER_XNA', 
    #'NAME_INCOME_TYPE_Maternity leave', 'NAME_FAMILY_STATUS_Unknown' from 
    # the raw training dataframe (X_train_raw) prior to having having PCA run on it.
    loc_code_gender_training_frame = X_train_raw.columns.get_loc('CODE_GENDER_XNA')
    loc_name_income_type_maternity_leave_training_frame = X_train_raw.columns.get_loc('NAME_INCOME_TYPE_Maternity leave')
    loc_name_family_status_unknown_training_frame = X_train_raw.columns.get_loc('NAME_FAMILY_STATUS_Unknown')

    # Insert each column into the testing dataframe at the same index it had 
    # in the X_train_raw dataframe before PCA was run. Fill each column with all 0s.
    # Order is important. 'CODE_GENDER_XNA' should be inserted first, followed by 
    # 'NAME_INCOME_TYPE_Maternity leave', and then finally 'NAME_FAMILY_STATUS_Unknown'.
    testing_dataframe.insert(loc=loc_code_gender_training_frame, column='CODE_GENDER_XNA', value=0)
    testing_dataframe.insert(loc=loc_name_income_type_maternity_leave_training_frame, column='NAME_INCOME_TYPE_Maternity leave', value=0)
    testing_dataframe.insert(loc=loc_name_family_status_unknown_training_frame, column='NAME_FAMILY_STATUS_Unknown', value=0)
    return testing_dataframe

def test_set_preprocessing_pipeline(testing_dataframe):
    """
    Recreate all features that were engineered in the training set during 
    the original data preprocessing phase. Missing numerical 'NaN' values 
    will be filled with an imputer. Missing binary categorical feature 'NaN' 
    values will be replaced with 0. The pipeline will also apply 
    the min-max and PCA transforms originally fit on features 
    in the training set to numerical features in the testing set.

    Parameters:
        testing_dataframe: Pandas dataframe containing a testing dataset
        
    Returns: a fully preprocessed testing dataframe
    """
    
    # Create the HAS_CHILDREN feature.
    CNT_CHILDREN_test = testing_dataframe['CNT_CHILDREN']
    HAS_CHILDREN = CNT_CHILDREN_test.map(lambda x: 1 if x > 0 else 0)

    # Append the newly engineered HAS_CHILDREN feature to the main dataframe.
    testing_dataframe = testing_dataframe.assign(HAS_CHILDREN=HAS_CHILDREN.values)
    
    # Drop the CNT_CHILDREN column from the main dataframe
    testing_dataframe = testing_dataframe.drop('CNT_CHILDREN', axis=1)

    # Create the NUMBER_FAMILY_MEMBERS feature. 
    CNT_FAM_MEMBERS_test = testing_dataframe['CNT_FAM_MEMBERS']
    NUMBER_FAMILY_MEMBERS = CNT_FAM_MEMBERS_test.map(lambda x: 'one' if x == 1 else ('two' if x == 2 else 'three_plus'))

    # Append the newly engineered FAMILY_SIZE feature to the main dataframe.
    testing_dataframe = testing_dataframe.assign(NUMBER_FAMILY_MEMBERS=NUMBER_FAMILY_MEMBERS.values)
    
    # Drop the CNT_FAM_MEMBERS feature from the main dataframe
    testing_dataframe = testing_dataframe.drop('CNT_FAM_MEMBERS', axis=1)

    # Build the HAS_CREDIT_BUREAU_LOANS_OVERDUE feature
    testing_dataframe = build_feature_HAS_CREDIT_BUREAU_LOANS_OVERDUE(testing_dataframe)
    
    # Create the HAS_JOB feature
    DAYS_EMPLOYED_test = testing_dataframe['DAYS_EMPLOYED']
    HAS_JOB = DAYS_EMPLOYED_test.map(lambda x: 1 if x <= 0 else 0)

    # Append the newly engineered FAMILY_SIZE feature to the main dataframe.
    testing_dataframe = testing_dataframe.assign(HAS_JOB=HAS_JOB.values)
    
    # Drop the DAYS_EMPLOYED feature from the main dataframe
    testing_dataframe = testing_dataframe.drop('DAYS_EMPLOYED', axis=1)
    
    # Translate the two negatively-valued features DAYS_REGISTRATION, and 
    # DAYS_LAST_PHONE_CHANGE to positive values
    translate_negative_valued_features(testing_dataframe, non_norm_feat_neg_values_skewed)
    
    # Log-transform all 17 non-normalized numerical features that have skewed distributions.
    testing_dataframe[log_transform_feats] = testing_dataframe[log_transform_feats].apply(lambda x: np.log(x + 1))
    
    # Create a list of all numerical features in the testing dataframe that have at least one 'NaN' entry
    numerical_features_with_nan_testing = testing_dataframe[numerical_features].columns[testing_dataframe[numerical_features].isna().any()].tolist()
    
    # Use an imputer to replace 'NaN' values for all numerical features with each feature's mean.
    testing_dataframe[numerical_features_with_nan_testing] = imputer.fit_transform(testing_dataframe[numerical_features_with_nan_testing])
    
    # Remove the borrower ID column, SK_ID_CURR, from the main dataframe
    testing_dataframe = testing_dataframe.drop('SK_ID_CURR', axis=1)
    
    # One-hot encode all 19 non-binary categorical features.
    testing_dataframe = pd.get_dummies(testing_dataframe, columns=cat_feat_need_one_hot)
    
    # After one-hot encoding, the testing dataframe from application_test.csv will be 
    # missing 2 columns that are in the training dataframe. It will also have an extra 
    # column that was not in the training dataframe, giving it 249 total columns. 
    # If this is the case, we need to modify this testing dataframe so that its columns 
    # and column order is consistent with the training dataframe.
    if testing_dataframe.shape[1] == 249:
        testing_dataframe = adjust_columns_application_test_csv_table(testing_dataframe)
    
    # Create a list of the binary categorical features with at least one 'NaN' entry
    bin_cat_feat_with_nan_testing = testing_dataframe[all_bin_cat_feat].columns[testing_dataframe[all_bin_cat_feat].isna().any()].tolist()
    
    # Replace each 'NaN' value in each of these binary features with 0
    testing_dataframe[bin_cat_feat_with_nan_testing] = testing_dataframe[bin_cat_feat_with_nan_testing].fillna(value=0)
    
    # Transform each of the 21 features that need to be scaled to the range [0,1] using 
    # the min-max scaler fit on the training set.
    testing_dataframe[feats_to_scale] = scaler.transform(testing_dataframe[feats_to_scale])
    
    # Reduce the testing dataframe to the top selectKbest features:
    testing_dataframe = testing_dataframe[selectK_top_features]
    
    return testing_dataframe

# 22. Preprocess the test validation set.
X_test_final = test_set_preprocessing_pipeline(X_test_raw)

# Verify that both the training and test validation dataframes have the expected number of columns after 
# preprocessing its data and reducing their featurespace to the top 30 features returned by SelectKBest.
print('Training set preprocessing complete. The final training dataframe now has {} columns. Expected: 30.'.format(X_train_final.shape[1]))
print('Test validation set preprocessing complete. The final test validation dataframe now has {} columns. Expected: 30.'.format(X_test_final.shape[1]))
Training set preprocessing complete. The final training dataframe now has 30 columns. Expected: 30.
Test validation set preprocessing complete. The final test validation dataframe now has 30 columns. Expected: 30.
In [640]:
# Train the classifiers and compute prediction probabilities.

# 1. Use a Gaussian Naive Bayes classifier to make predictions on 
# the test validation set. Calculate the area under ROC curve score of 
# these predictions.

# Fit a Gaussian Naive Bayes classifier to the training dataframe.
clf_naive_bayes = GaussianNB()
clf_naive_bayes.fit(X_train_final, y_train)

# The Naive Bayes estimates of probability of the positive class (TARGET=1):   
# the probability estimate of each borrower making at least one late loan payment.
naive_bayes_selectKbest_y_score = clf_naive_bayes.predict_proba(X_test_final)[:, 1]

# The area under the ROC curve between the true target values and the 
# probability estimates of the predicted values.
naive_bayes_selectKbest_roc_auc_score = roc_auc_scorer(y_test, naive_bayes_selectKbest_y_score)

# Add the Naive Bayes classifier's scores to the results list.
y_score_list.append(naive_bayes_selectKbest_y_score)
clf_label_list.append('Naive Bayes SelectKBest, K=30')

print('Naive Bayes (SelectKBest) test validation set predictions\' ROC AUC score: {}'.format(naive_bayes_selectKbest_roc_auc_score))

# 2. Use an AdaBoost classifier to make predictions on the test validation set. 
# Calculate the area under ROC curve score of these predictions.

# Fit the AdaBoost classifier, using the parameter for 'n_estimators' discovered 
# when running GridSearchCV on AdaBoost above for the full featureset.
clf_AdaBoost = AdaBoostClassifier(learning_rate=1.0, n_estimators=1000, random_state=42)
clf_AdaBoost.fit(X_train_final, y_train)

# The AdaBoost classifier's estimates of probability of the positive class (TARGET=1):  
# the probability estimate of each borrower making at least one late loan payment.
adaBoost_selectKbest_y_score = clf_AdaBoost.predict_proba(X_test_final)[:, 1]

# The area under the ROC curve between the true target values and the 
# probability estimates of the predicted values.
adaBoost_selectKbest_roc_auc_score = roc_auc_scorer(y_test, adaBoost_selectKbest_y_score)

# Add the AdaBoost classifier's scores to the results list.
y_score_list.append(adaBoost_selectKbest_y_score)
clf_label_list.append('AdaBoost SelectKBest, K=30')

print('AdaBoost (SelectKBest) test validation set predictions\' ROC AUC score: {}'.format(adaBoost_selectKbest_roc_auc_score))

# 3. Try using a Logistic Regression classifier to make predictions.

# Fit the classifier to the training data.
clf_logistic_regression = LogisticRegression(penalty='l2', random_state=42, solver='liblinear', max_iter=100)
clf_logistic_regression.fit(X_train_final, y_train)

# The logistical regression classifier's estimates of probability of the positive 
# class (TARGET=1): the probability estimate of each borrower making at least one 
# late loan payment.
logistic_regression_selectKbest_y_score = clf_logistic_regression.predict_proba(X_test_final)[:, 1]

# The area under the ROC curve between the true target values and the 
# probability estimates of the predicted values.
logistic_regression_selectKbest_roc_auc_score = roc_auc_scorer(y_test, logistic_regression_selectKbest_y_score)

# Add the Logistic Regression classifier's scores to the results list.
y_score_list.append(logistic_regression_selectKbest_y_score)
clf_label_list.append('Logistic Regression SelectKBest, K=30')

print('Logistic Regression (SelectKBest) test validation set predictions\' ROC AUC score: {}'.format(logistic_regression_selectKbest_roc_auc_score))

# 4. Try using a Multi-layer Perceptron classifier to make predictions.

# Fit the classifier to the training data.
clf_mlp = MLPClassifier(
    hidden_layer_sizes=100, activation='identity', solver='adam', alpha=0.001, batch_size=200, 
    learning_rate_init=0.001, random_state=42
)
clf_mlp.fit(X_train_final, y_train)

# The multi-layer perceptron classifier's estimates of probability of the positive 
# class (TARGET=1): the probability estimate of each borrower making at least one 
# late loan payment.
mlp_selectKbest_y_score = clf_mlp.predict_proba(X_test_final)[:, 1]

# The area under the ROC curve between the true target values and the 
# probability estimates of the predicted values.
mlp_selectKbest_roc_auc_score = roc_auc_scorer(y_test, mlp_selectKbest_y_score)

# Add the Multi-Layer Perceptron classifier's scores to the results list.
y_score_list.append(mlp_selectKbest_y_score)
clf_label_list.append('Multi-Layer Perceptron SelectKBest, K=30')

print('Multi-layer Perceptron (SelectKBest) test validation set predictions\' ROC AUC score: {}'.format(mlp_selectKbest_roc_auc_score))

# 5. Try using a LightGBM classifier.

# Convert preprocessed training dataset into LightGBM dataset format
lightgbm_training = lgb.Dataset(X_train_final, label=y_train)

# Specify parameters
params = {}
params['learning_rate'] = 0.01
params['boosting_type'] = 'gbdt'
params['objective'] = 'binary'
params['metric'] = 'auc'
params['sub_feature'] = 0.3
params['num_leaves'] = 100
params['min_data_in_leaf'] = 500
params['max_depth'] = 10
params['max_bin'] = 64
#params['min_data_in_bin'] = 3
#params['lambda_l1'] = 0.01
params['lambda_l2'] = 0.01
#params['min_gain_to_split'] = 0.01
params['bagging_freq'] = 100
params['bagging_fraction'] = 0.9
#params['feature_fraction'] = 0.5

# Fit the LightGBM classifier to the training data
clf_lgb = lgb.train(params, lightgbm_training, 1500)

# Classifier's estimates of probability of the positive class (TARGET=1): the 
# probability estimate of each borrower making at least one late loan payment.
lgb_selectKbest_y_score = clf_lgb.predict(X_test_final)

# The area under the ROC curve between the true target values and the 
# probability estimates of the predicted values.
lgb_selectKbest_roc_auc_score = roc_auc_scorer(y_test, lgb_selectKbest_y_score)

# Add the LightGBM classifier's scores to the results list.
y_score_list.append(lgb_selectKbest_y_score)
clf_label_list.append('LightGBM SelectKBest, K=30')

print('LightGBM (SelectKBest) test validation set predictions\' ROC AUC score: {}'.format(lgb_selectKbest_roc_auc_score))
Naive Bayes (SelectKBest) test validation set predictions' ROC AUC score: 0.6748662184461512
AdaBoost (SelectKBest) test validation set predictions' ROC AUC score: 0.7332167998294561
Logistic Regression (SelectKBest) test validation set predictions' ROC AUC score: 0.7367170423891469
Multi-layer Perceptron (SelectKBest) test validation set predictions' ROC AUC score: 0.7358901049573279
LightGBM (SelectKBest) test validation set predictions' ROC AUC score: 0.7394787228642934

GridSearchCV Tuning of LightGBM classifier trained on full featureset

In [654]:
# Step 3. Train a LightGBM classifier on the full featureset, and use GridSearchCV 
# to develop further intuition on LightGBM parameter tuning.

# Load the main data tables
application_train_data = pd.read_csv("data/application_train.csv")
application_test_data = pd.read_csv("data/application_test.csv")

# Load the Bureau data table
bureau_data = pd.read_csv("data/bureau.csv")

# 1: Create lists of different feature types in the main data 
# frame, based on how each type will need to be preprocessed.

# i. All 18 categorical features needing one-hot encoding. 
#    Includes the 4 categorical features originally 
#    mis-identified as having been normalized:
#    EMERGENCYSTATE_MODE, HOUSETYPE_MODE, WALLSMATERIAL_MODE, 
#    FONDKAPREMONT_MODE
cat_feat_need_one_hot = [
    'NAME_CONTRACT_TYPE', 'CODE_GENDER', 'FLAG_OWN_CAR', 
    'FLAG_OWN_REALTY', 'NAME_INCOME_TYPE', 'NAME_EDUCATION_TYPE', 
    'NAME_FAMILY_STATUS', 'NAME_HOUSING_TYPE', 'REGION_RATING_CLIENT',
    'REGION_RATING_CLIENT_W_CITY', 'WEEKDAY_APPR_PROCESS_START', 'ORGANIZATION_TYPE',
    'NAME_TYPE_SUITE', 'OCCUPATION_TYPE', 'EMERGENCYSTATE_MODE',
    'HOUSETYPE_MODE', 'WALLSMATERIAL_MODE', 'FONDKAPREMONT_MODE'
]

# ii. All 32 binary categorical features already one-hot encoded.
bin_cat_feat = [
    'FLAG_MOBIL', 'FLAG_EMP_PHONE', 'FLAG_WORK_PHONE',
    'FLAG_CONT_MOBILE', 'FLAG_PHONE', 'FLAG_EMAIL',
    'REG_REGION_NOT_LIVE_REGION', 'REG_REGION_NOT_WORK_REGION', 'LIVE_REGION_NOT_WORK_REGION',
    'REG_CITY_NOT_LIVE_CITY', 'REG_CITY_NOT_WORK_CITY', 'LIVE_CITY_NOT_WORK_CITY',
    'FLAG_DOCUMENT_2', 'FLAG_DOCUMENT_3', 'FLAG_DOCUMENT_4',
    'FLAG_DOCUMENT_5', 'FLAG_DOCUMENT_6', 'FLAG_DOCUMENT_7',
    'FLAG_DOCUMENT_8', 'FLAG_DOCUMENT_9', 'FLAG_DOCUMENT_10',
    'FLAG_DOCUMENT_11', 'FLAG_DOCUMENT_12', 'FLAG_DOCUMENT_13',
    'FLAG_DOCUMENT_14', 'FLAG_DOCUMENT_15', 'FLAG_DOCUMENT_16',
    'FLAG_DOCUMENT_17', 'FLAG_DOCUMENT_18', 'FLAG_DOCUMENT_19',
    'FLAG_DOCUMENT_20', 'FLAG_DOCUMENT_21'
]

# iii. All 2 non-normalized numerical features with skewed distributions 
#    and negative values. These features will need to have their 
#    distributions translated to positive ranges before being 
#    log-transformed, and then later scaled to the range [0,1].
non_norm_feat_neg_values_skewed = [
    'DAYS_REGISTRATION', 'DAYS_LAST_PHONE_CHANGE'
]


# iv. All 15 non-normalized numerical features with skewed distributions, 
#    and only positive values. These features will need to be 
#    log-transformed, and eventually scaled to the range [0,1].
non_norm_feat_pos_values_skewed = [
    'AMT_INCOME_TOTAL', 'AMT_CREDIT', 'AMT_ANNUITY',
    'AMT_GOODS_PRICE', 'OBS_30_CNT_SOCIAL_CIRCLE', 'DEF_30_CNT_SOCIAL_CIRCLE',
    'OBS_60_CNT_SOCIAL_CIRCLE', 'DEF_60_CNT_SOCIAL_CIRCLE', 'AMT_REQ_CREDIT_BUREAU_HOUR',
    'AMT_REQ_CREDIT_BUREAU_DAY', 'AMT_REQ_CREDIT_BUREAU_WEEK', 'AMT_REQ_CREDIT_BUREAU_MON',
    'AMT_REQ_CREDIT_BUREAU_QRT', 'AMT_REQ_CREDIT_BUREAU_YEAR', 'OWN_CAR_AGE'
]

# v. All 4 numerical features with normal shapes but needing to be scaled 
#    to the range [0,1].
norm_feat_need_scaling = [
    'DAYS_BIRTH', 'DAYS_ID_PUBLISH', 'HOUR_APPR_PROCESS_START',
    'REGION_POPULATION_RELATIVE'
]

# vi. All 46 numerical features that have been normalized to the range 
#   [0,1]. These features will need neither log-transformation, nor 
#   any further scaling.
norm_feat_not_need_scaling = [
    'EXT_SOURCE_2', 'EXT_SOURCE_3', 'YEARS_BEGINEXPLUATATION_AVG',
    'YEARS_BEGINEXPLUATATION_MODE', 'YEARS_BEGINEXPLUATATION_MEDI', 'FLOORSMAX_AVG', 
    'FLOORSMAX_MODE', 'FLOORSMAX_MEDI', 'LIVINGAREA_AVG',
    'LIVINGAREA_MODE', 'LIVINGAREA_MEDI', 'ENTRANCES_AVG',
    'ENTRANCES_MODE', 'ENTRANCES_MEDI', 'APARTMENTS_AVG',
    'APARTMENTS_MODE', 'APARTMENTS_MEDI', 'ELEVATORS_AVG', 
    'ELEVATORS_MODE', 'ELEVATORS_MEDI', 'NONLIVINGAREA_AVG',
    'NONLIVINGAREA_MODE', 'NONLIVINGAREA_MEDI', 'EXT_SOURCE_1',
    'BASEMENTAREA_AVG', 'BASEMENTAREA_MODE', 'BASEMENTAREA_MEDI',
    'LANDAREA_AVG', 'LANDAREA_MODE', 'LANDAREA_MEDI',
    'YEARS_BUILD_AVG', 'YEARS_BUILD_MODE', 'YEARS_BUILD_MEDI',
    'FLOORSMIN_AVG', 'FLOORSMIN_MODE', 'FLOORSMIN_MEDI',
    'LIVINGAPARTMENTS_AVG', 'LIVINGAPARTMENTS_MODE', 'LIVINGAPARTMENTS_MEDI',
    'NONLIVINGAPARTMENTS_AVG', 'NONLIVINGAPARTMENTS_MODE', 'NONLIVINGAPARTMENTS_MEDI',
    'COMMONAREA_AVG', 'COMMONAREA_MODE', 'COMMONAREA_MEDI',
    'TOTALAREA_MODE'
]

# vii. The remaining 3 features in the main data frame that will be 
#    re-engineered and transformed into different features
feat_to_be_reengineered = [
    'CNT_CHILDREN', 'CNT_FAM_MEMBERS', 'DAYS_EMPLOYED'
]

# 2: Separate target data from training dataset.
targets = application_train_data['TARGET']
features_raw = application_train_data.drop('TARGET', axis = 1)

# 3: Use train_test_split from sklearn.cross_validation to 
# create a test validation set that is 20% of the size of the total training set: 
# Will allow me to compare performance of various learning algorithms without 
# overfitting to the training data.
X_train_raw, X_test_raw, y_train, y_test = train_test_split(features_raw, 
                                                    targets, 
                                                    test_size = 0.2, 
                                                    random_state = 42)

# 4: Use the CNT_CHILDREN feature to engineer a binary 
# categorical feature called HAS_CHILDREN. If value of CNT_CHILDREN is 
# greater than 0, the value of HAS_CHILDREN will be 1. If value of CNT_CHILDREN is 
# 0, value of HAS_CHILDREN will be 0. 
CNT_CHILDREN_train = X_train_raw['CNT_CHILDREN']
HAS_CHILDREN = CNT_CHILDREN_train.map(lambda x: 1 if x > 0 else 0)

# Append the newly engineered HAS_CHILDREN feature to the main dataframe.
X_train_raw = X_train_raw.assign(HAS_CHILDREN=HAS_CHILDREN.values)

# 5: Drop the CNT_CHILDREN column from the main dataframe
X_train_raw = X_train_raw.drop('CNT_CHILDREN', axis=1)

# Add the new HAS_CHILDREN feature to the list of binary categorical 
# features that are already one-hot encoded. There are now 33 such features.
bin_cat_feat = bin_cat_feat + ['HAS_CHILDREN']

# 6. Use the CNT_FAM_MEMBERS feature to engineer a categorical feature called NUMBER_FAMILY_MEMBERS. 
# If CNT_FAM_MEMBERS is 1.0, then the value of NUMBER_FAMILY_MEMBERS will be 'one'. If CNT_FAM_MEMBERS is 2.0, 
# then NUMBER_FAMILY_MEMBERS will be 'two'. If CNT_FAM_MEMBERS is 3.0 or greater, then NUMBER_FAMILY_MEMBERS will 
# be 'three_plus'.
CNT_FAM_MEMBERS_train = X_train_raw['CNT_FAM_MEMBERS']
NUMBER_FAMILY_MEMBERS = CNT_FAM_MEMBERS_train.map(lambda x: 'one' if x == 1 else ('two' if x == 2 else 'three_plus'))

# Append the newly engineered FAMILY_SIZE feature to the main dataframe.
X_train_raw = X_train_raw.assign(NUMBER_FAMILY_MEMBERS=NUMBER_FAMILY_MEMBERS.values)

# 7. Drop the CNT_FAM_MEMBERS feature from the main dataframe
X_train_raw = X_train_raw.drop('CNT_FAM_MEMBERS', axis=1)

# Add the new NUMBER_FAMILY_MEMBERS feature to the list of categorical 
# features that will need to be one-hot encoded. There are now 19 of these features.
cat_feat_need_one_hot = cat_feat_need_one_hot + ['NUMBER_FAMILY_MEMBERS']

# 8. Use the CREDIT_DAY_OVERDUE feature in bureau.csv to engineer the binary 
# categorical HAS_CREDIT_BUREAU_LOANS_OVERDUE feature. If CREDIT_DAY_OVERDUE for a 
# particular borrower ID (SK_ID_CURR) is greater than 0, then the value of 
# HAS_CREDIT_BUREAU_LOANS_OVERDUE will be 1. If CREDIT_DAY_OVERDUE for a particular 
# borrower ID is 0, then the value of HAS_CREDIT_BUREAU_LOANS_OVERDUE will be 0.

# Filter the bureau data table for loans which are overdue (have a value 
# for CREDIT_DAY_OVERDUE that's greater than 0)
bureau_data_filtered_for_overdue = bureau_data[bureau_data['CREDIT_DAY_OVERDUE'] > 0]

def build_feature_HAS_CREDIT_BUREAU_LOANS_OVERDUE(dataframe):
    """
    Use the CREDIT_DAY_OVERDUE feature in bureau.csv to engineer the binary 
    categorical HAS_CREDIT_BUREAU_LOANS_OVERDUE feature. If CREDIT_DAY_OVERDUE for a 
    particular borrower ID (SK_ID_CURR) is greater than 0, then the value of 
    HAS_CREDIT_BUREAU_LOANS_OVERDUE will be 1. If CREDIT_DAY_OVERDUE for a particular 
    borrower ID is 0, then the value of HAS_CREDIT_BUREAU_LOANS_OVERDUE will be 0.

    Parameters:
        dataframe: Pandas dataframe containing a training or testing dataset
        
    Returns: The dataframe with HAS_CREDIT_BUREAU_LOANS_OVERDUE feature appended to it.
    """
    # Create a series called HAS_CREDIT_BUREAU_LOANS_OVERDUE and fill it with zeros.  
    # Its index is identical to that of the main dataframe. It will eventually be appended 
    # to the main data frame as a column.
    HAS_CREDIT_BUREAU_LOANS_OVERDUE = pd.Series(data=0, index = dataframe['SK_ID_CURR'].index)

    # A list of all the borrowers IDs in the main dataframe
    main_data_table_borrower_IDs = dataframe['SK_ID_CURR'].values

    # For each loan in the bureau data table that is overdue 
    # (has a value for CREDIT_DAY_OVERDUE that's greater than 0)
    for index, row in bureau_data_filtered_for_overdue.iterrows():
        # The borrower ID (SK_ID_CURR) that owns the overdue loan
        borrower_ID = row['SK_ID_CURR']
        # If the borrower ID owning the overdue loan is also 
        # in the main data frame, then enter a value of 1 in 
        # the series HAS_CREDIT_BUREAU_LOANS_OVERDUE at an index 
        # that is identical to the index of the borrower ID 
        # in the main data frame.
        if borrower_ID in main_data_table_borrower_IDs:
            # The index of the borrower's row in the main data table.
            borrower_index_main_data_table = dataframe.index[dataframe['SK_ID_CURR'] == borrower_ID].tolist()[0]
            # Place a value of 1 at the index of the series HAS_CREDIT_BUREAU_LOANS_OVERDUE 
            # which corresponds to the index of the borrower's ID in the main data table.
            HAS_CREDIT_BUREAU_LOANS_OVERDUE.loc[borrower_index_main_data_table] = 1
    # Append the newly engineered HAS_CREDIT_BUREAU_LOANS_OVERDUE feature to the main dataframe.
    dataframe = dataframe.assign(HAS_CREDIT_BUREAU_LOANS_OVERDUE=HAS_CREDIT_BUREAU_LOANS_OVERDUE.values)
    return dataframe

# Build the HAS_CREDIT_BUREAU_LOANS_OVERDUE feature
X_train_raw = build_feature_HAS_CREDIT_BUREAU_LOANS_OVERDUE(X_train_raw)

# Add the new HAS_CREDIT_BUREAU_LOANS_OVERDUE feature to the list of binary categorical 
# features. There are now 34 of these features.
bin_cat_feat = bin_cat_feat + ['HAS_CREDIT_BUREAU_LOANS_OVERDUE']

# 9. Use the DAYS_EMPLOYED feature to engineer a binary categorical feature called HAS_JOB. 
# If the value of DAYS_EMPLOYED is 0 or less, then HAS_JOB will be 1. Otherwise, HAS_JOB will 
# be 0. This condition will apply to all borrowers who had a value of 365243 for DAYS_EMPLOYED, 
# which I hypothesized can be best interpreted as meaning that a borrower does not have a job.
DAYS_EMPLOYED_train = X_train_raw['DAYS_EMPLOYED']
HAS_JOB = DAYS_EMPLOYED_train.map(lambda x: 1 if x <= 0 else 0)

# Append the newly engineered FAMILY_SIZE feature to the main dataframe.
X_train_raw = X_train_raw.assign(HAS_JOB=HAS_JOB.values)

# 10. Drop the DAYS_EMPLOYED feature from the main dataframe
X_train_raw = X_train_raw.drop('DAYS_EMPLOYED', axis=1)

# Add the new HAS_JOB feature to the list of binary categorical features. 
# There are now 35 of these features.
bin_cat_feat = bin_cat_feat + ['HAS_JOB']

# 11. Translate the 2 non-normalized numerical features that have skewed distributions 
# and negative values: DAYS_REGISTRATION, and DAYS_LAST_PHONE_CHANGE

def translate_negative_valued_features(dataframe, feature_name_list):
    """
    Translate a dataset's continuous features containing several negative 
    values. The dataframe is modified such that all values of each feature 
    listed in the feature_name_list parameter become positive.

    Parameters:
        dataframe: Pandas dataframe containing the features
        feature_name_list: List of strings, containing the names 
                           of each feature whose values will be 
                           translated
    """
    for feature in feature_name_list:
        # The minimum, most-negative, value of the feature
        feature_min_value = dataframe[feature].min()
        # Translate each value of the feature in a positive direction, 
        # of magnitude that's equal to the feature's most negative value.
        dataframe[feature] = dataframe[feature].apply(lambda x: x - feature_min_value)

# Translate the above two negatively-valued features to positive values
translate_negative_valued_features(X_train_raw, non_norm_feat_neg_values_skewed)

# 12. Log-transform all 17 non-normalized numerical features that have skewed distributions. 
# These 17 features include the 2 that were translated to positive ranges in Step 11.

# Add the 2 features translated to positive ranges above in Step 11 to 
# the list of non-normalized skewed features with positive values. This is 
# the set of features that will be log-transformed
log_transform_feats = non_norm_feat_pos_values_skewed + non_norm_feat_neg_values_skewed

X_train_raw[log_transform_feats] = X_train_raw[log_transform_feats].apply(lambda x: np.log(x + 1))

# 13. Replace 'NaN' values for all numerical features with each feature's mean. Fit an imputer 
# to each numerical feature containing at least one 'NaN' entry.

# Create a list of all the 67 numerical features in the main dataframe. These include all 
# 17 features that were log-transformed in Step 12, as well as the 4 normal features that 
# still need to be scaled, as well as the 46 normal features that don't need scaling.
numerical_features = log_transform_feats + norm_feat_need_scaling + norm_feat_not_need_scaling

# Create a list of all numerical features in the training set that have at least one 'NaN' entry
numerical_features_with_nan = X_train_raw[numerical_features].columns[X_train_raw[numerical_features].isna().any()].tolist()

# Create an imputer
imputer = Imputer()
# Fit the imputer to each numerical feature in the training set that has 'NaN' values, 
# and replace each 'NaN' entry of each feature with that feature's mean.
X_train_raw[numerical_features_with_nan] = imputer.fit_transform(X_train_raw[numerical_features_with_nan])

# 14. Remove the borrower ID column, SK_ID_CURR, from the main dataframe
X_train_raw = X_train_raw.drop('SK_ID_CURR', axis=1)

# 15. One-hot encode all 19 non-binary categorical features.
X_train_raw = pd.get_dummies(X_train_raw, columns=cat_feat_need_one_hot)

# Create a list that includes only the newly one-hot encoded features 
# as well as all the categorical features that were already binary.
all_bin_cat_feat = X_train_raw.columns.tolist()
for column_name in X_train_raw[numerical_features].columns.tolist():
    all_bin_cat_feat.remove(column_name)

# 16. Replace all 'NaN' values in all binary categorical features with 0.

# Create a list of binary categorical features with at least one 'NaN' entry
bin_cat_feat_with_nan = X_train_raw[all_bin_cat_feat].columns[X_train_raw[all_bin_cat_feat].isna().any()].tolist()

# Replace each 'NaN' value in each of these binary features with 0
X_train_raw[bin_cat_feat_with_nan] = X_train_raw[bin_cat_feat_with_nan].fillna(value=0)

# 17. Fit a min-max scaler to each of the 17 log-transformed numerical features, as well 
# as to the 4 features DAYS_BIRTH, DAYS_ID_PUBLISH, HOUR_APPR_PROCESS_START, and the normalized 
# feature REGION_POPULATION_RELATIVE. Each feature will be scaled to a range [0.0, 1.0].

# Build a list of all 21 features needing scaling. Add the list of features that 
# were log-normalized above in Step 12 to the list of normally shaped features 
# that need to be scaled to the range [0,1].
feats_to_scale = norm_feat_need_scaling + log_transform_feats

# Initialize a scaler with the default range of [0,1]
scaler = MinMaxScaler()

# Fit the scaler to each of the features of the train set that need to be scaled, 
# then transform each of these features' values to the new scale.
X_train_raw[feats_to_scale] = scaler.fit_transform(X_train_raw[feats_to_scale])

# Rename the dataframe to indicate that its columns have been fully preprocessed.
X_train_final = X_train_raw
In [648]:
# 18. Build a data preprocessing pipeline to used for all testing sets. 
# This pipeline will recreate all features that were engineered in the 
# training set during the original data preprocessing phase. 
# The pipeline will also apply the imputer, min-max, and PCA transforms 
# originally fit on features in the training set to all datapoints in a 
# testing set.

def adjust_columns_application_test_csv_table(testing_dataframe):
    """
    After it is one-hot encoded, application_test.csv data table will have one 
    extra column, 'REGION_RATING_CLIENT_W_CITY_-1', that is not present in the 
    training dataframe. This column will be removed from the testing datatable 
    in this case. Only 1 of the 48,744 rows in application_test.csv will have a 
    value of 1 for this feature following one-hot encoding. I am not worried 
    about this column's elimination from the testing dataframe affecting predictions.
    
    Additionally, unlike the test validation set, which originally comprised 20% of 
    application_train.csv, application_test.csv will be missing the following columns 
    after it is one-hot encoded:
    
    'CODE_GENDER_XNA', 'NAME_INCOME_TYPE_Maternity leave', 'NAME_FAMILY_STATUS_Unknown'
    
    In this case, we need to insert these columns into the testing dataframe, at 
    the exact same indices they are located at in the fully preprocessed training 
    dataframe.Each inserted column will be filled with all zeros. (If each of these 
    binary features are missing from the application_test.csv data table, we can infer 
    that each borrower in thatdata table obviously would have a 0 for each feature were 
    it present.)

    Parameters:
        testing_dataframe: Pandas dataframe containing the testing dataset
                           contained in the file application_test.csv
        
    Returns: a testing dataframe containing the exact same columns and 
             column order as found in the training dataframe
    """
    
    # Identify any columns in the one-hot encoded testing_dataframe that 
    # are not in X_train_raw. These columns will need to be removed from the
    # testing_dataframe. (Expected that there will only be one such 
    # column: 'REGION_RATING_CLIENT_W_CITY_-1')
    X_train_columns_list = X_train_raw.columns.tolist()
    testing_dataframe_columns_list = testing_dataframe.columns.tolist()
    for column_name in X_train_columns_list:
        if column_name in testing_dataframe_columns_list:
            testing_dataframe_columns_list.remove(column_name)
            columns_not_in_X_train_raw = testing_dataframe_columns_list
    
    # Drop any column from the testing_dataframe that is not in the 
    # training dataframe. Expected to only be the one column 'REGION_RATING_CLIENT_W_CITY_-1'
    for column in columns_not_in_X_train_raw:
        testing_dataframe = testing_dataframe.drop(column, axis=1)
    
    # Get the column indices of each of the features 'CODE_GENDER_XNA', 
    #'NAME_INCOME_TYPE_Maternity leave', 'NAME_FAMILY_STATUS_Unknown' from 
    # the raw training dataframe (X_train_raw) prior to having having PCA run on it.
    loc_code_gender_training_frame = X_train_raw.columns.get_loc('CODE_GENDER_XNA')
    loc_name_income_type_maternity_leave_training_frame = X_train_raw.columns.get_loc('NAME_INCOME_TYPE_Maternity leave')
    loc_name_family_status_unknown_training_frame = X_train_raw.columns.get_loc('NAME_FAMILY_STATUS_Unknown')

    # Insert each column into the testing dataframe at the same index it had 
    # in the X_train_raw dataframe before PCA was run. Fill each column with all 0s.
    # Order is important. 'CODE_GENDER_XNA' should be inserted first, followed by 
    # 'NAME_INCOME_TYPE_Maternity leave', and then finally 'NAME_FAMILY_STATUS_Unknown'.
    testing_dataframe.insert(loc=loc_code_gender_training_frame, column='CODE_GENDER_XNA', value=0)
    testing_dataframe.insert(loc=loc_name_income_type_maternity_leave_training_frame, column='NAME_INCOME_TYPE_Maternity leave', value=0)
    testing_dataframe.insert(loc=loc_name_family_status_unknown_training_frame, column='NAME_FAMILY_STATUS_Unknown', value=0)
    return testing_dataframe

def test_set_preprocessing_pipeline(testing_dataframe):
    """
    Recreate all features that were engineered in the training set during 
    the original data preprocessing phase. The pipeline will also apply 
    an imputer to the test data table fill 'NaN' values. Binary feature's 'Nan' 
    values will be filled with 0. The min-max scaler fit on features 
    in the training set will be applied to the numerical features in the testing set.

    Parameters:
        testing_dataframe: Pandas dataframe containing a testing dataset
        
    Returns: a fully preprocessed testing dataframe
    """
    
    # Create the HAS_CHILDREN feature.
    CNT_CHILDREN_test = testing_dataframe['CNT_CHILDREN']
    HAS_CHILDREN = CNT_CHILDREN_test.map(lambda x: 1 if x > 0 else 0)

    # Append the newly engineered HAS_CHILDREN feature to the main dataframe.
    testing_dataframe = testing_dataframe.assign(HAS_CHILDREN=HAS_CHILDREN.values)
    
    # Drop the CNT_CHILDREN column from the main dataframe
    testing_dataframe = testing_dataframe.drop('CNT_CHILDREN', axis=1)

    # Create the NUMBER_FAMILY_MEMBERS feature. 
    CNT_FAM_MEMBERS_test = testing_dataframe['CNT_FAM_MEMBERS']
    NUMBER_FAMILY_MEMBERS = CNT_FAM_MEMBERS_test.map(lambda x: 'one' if x == 1 else ('two' if x == 2 else 'three_plus'))

    # Append the newly engineered FAMILY_SIZE feature to the main dataframe.
    testing_dataframe = testing_dataframe.assign(NUMBER_FAMILY_MEMBERS=NUMBER_FAMILY_MEMBERS.values)
    
    # Drop the CNT_FAM_MEMBERS feature from the main dataframe
    testing_dataframe = testing_dataframe.drop('CNT_FAM_MEMBERS', axis=1)

    # Build the HAS_CREDIT_BUREAU_LOANS_OVERDUE feature
    testing_dataframe = build_feature_HAS_CREDIT_BUREAU_LOANS_OVERDUE(testing_dataframe)
    
    # Create the HAS_JOB feature
    DAYS_EMPLOYED_test = testing_dataframe['DAYS_EMPLOYED']
    HAS_JOB = DAYS_EMPLOYED_test.map(lambda x: 1 if x <= 0 else 0)

    # Append the newly engineered FAMILY_SIZE feature to the main dataframe.
    testing_dataframe = testing_dataframe.assign(HAS_JOB=HAS_JOB.values)
    
    # Drop the DAYS_EMPLOYED feature from the main dataframe
    testing_dataframe = testing_dataframe.drop('DAYS_EMPLOYED', axis=1)
    
    # Translate the two negatively-valued features DAYS_REGISTRATION, and 
    # DAYS_LAST_PHONE_CHANGE to positive values
    translate_negative_valued_features(testing_dataframe, non_norm_feat_neg_values_skewed)
    
    # Log-transform all 17 non-normalized numerical features that have skewed distributions.
    testing_dataframe[log_transform_feats] = testing_dataframe[log_transform_feats].apply(lambda x: np.log(x + 1))
    
    # Create a list of all numerical features in the testing dataframe that have at least one 'NaN' entry
    numerical_features_with_nan_testing = testing_dataframe[numerical_features].columns[testing_dataframe[numerical_features].isna().any()].tolist()
    
    # Use an imputer to replace 'NaN' values for all numerical features with each feature's mean.
    testing_dataframe[numerical_features_with_nan_testing] = imputer.fit_transform(testing_dataframe[numerical_features_with_nan_testing])
    
    # Remove the borrower ID column, SK_ID_CURR, from the main dataframe
    testing_dataframe = testing_dataframe.drop('SK_ID_CURR', axis=1)
    
    # One-hot encode all 19 non-binary categorical features.
    testing_dataframe = pd.get_dummies(testing_dataframe, columns=cat_feat_need_one_hot)

    # After one-hot encoding, the testing dataframe from application_test.csv will be 
    # missing 2 columns that are in the training dataframe. It will also have an extra 
    # column that was not in the training dataframe, giving it 249 total columns. 
    # If this is the case, we need to modify this testing dataframe so that its columns 
    # and column order is consistent with the training dataframe.
    if testing_dataframe.shape[1] == 249:
        testing_dataframe = adjust_columns_application_test_csv_table(testing_dataframe)
    
    # Create a list of the binary categorical features with at least one 'NaN' entry
    bin_cat_feat_with_nan_testing = testing_dataframe[all_bin_cat_feat].columns[testing_dataframe[all_bin_cat_feat].isna().any()].tolist()
    
    # Replace each 'NaN' value in each of these binary features with 0
    testing_dataframe[bin_cat_feat_with_nan_testing] = testing_dataframe[bin_cat_feat_with_nan_testing].fillna(value=0)
     
    # Transform each of the 21 features that need to be scaled to the range [0,1] using 
    # the min-max scaler fit on the training set.
    testing_dataframe[feats_to_scale] = scaler.transform(testing_dataframe[feats_to_scale])
    
    return testing_dataframe

# 19. Preprocess the test validation set.
X_test_final = test_set_preprocessing_pipeline(X_test_raw)

# Verify that both the training and test validation dataframes have the expected number of columns after 
# preprocessing its data and reducing their featurespace to the top 30 features returned by SelectKBest.
print('Training set preprocessing complete. The final training dataframe now has {} columns. Expected: 251.'.format(X_train_final.shape[1]))
print('Test validation set preprocessing complete. The final test validation dataframe now has {} columns. Expected: 251.'.format(X_test_final.shape[1]))
Training set preprocessing complete. The final training dataframe now has 251 columns. Expected: 251.
Test validation set preprocessing complete. The final test validation dataframe now has 251 columns. Expected: 251.
In [433]:
# Performs GridSearchCV on a LightGBM classifier learning 
# algorithm to gain further intuition to aid in hyperparameter 
# tuning.

# # Create cross-validation sets from the training data
# cv_sets = StratifiedKFold(n_splits = 5, random_state = 42)

# Transform 'roc_auc_scorer' into a scoring function using 'make_scorer' 
scoring_fnc = make_scorer(roc_auc_scorer)

# Create an LightGBM classifier object
clf = lgb.LGBMClassifier(learning_rate = 0.1,
                         boosting_type = 'gbdt',
                         objective = 'binary',
                         metric = 'auc',
                         sub_feature = 0.3,
                         num_leaves = 50,
                         min_data_in_leaf = 500,
                         max_depth = -1,
                         max_bin = 100,
                         lambda_l2 = 0.1,
                         bagging_freq = 3,
                         bagging_fraction = 0.9,
                         random_state = 42,
                        )

# The parameters to search
grid_params = {
    'learning_rate': [0.001, 0.01, 0.1],
    'sub_feature': [0.3],
    'num_leaves': [200],
    'lambda_l2': [0.1],
    'min_data_in_leaf': [40],
    'max_depth': [-1]
}

# Create a GridSearchCV object.
grid = GridSearchCV(clf, grid_params, scoring_fnc, cv=3)

# Fit the grid search object to the data to compute the optimal model
grid.fit(X_train_final, y_train)

# Print the best parameters found
print('Best hyperparameter combo:\r')
print(grid.best_params_)
print('ROC AUC score of best hyperparameter combo:\r')
print(roc_auc_score(y_test, grid.predict_proba(X_test_final)[:,1]))
/anaconda3/envs/home_credit_default_risk_competition/lib/python3.5/site-packages/lightgbm/engine.py:99: UserWarning: Found `num_iterations` in params. Will use it instead of argument
  warnings.warn("Found `{}` in params. Will use it instead of argument".format(alias))
/anaconda3/envs/home_credit_default_risk_competition/lib/python3.5/site-packages/lightgbm/engine.py:99: UserWarning: Found `num_iterations` in params. Will use it instead of argument
  warnings.warn("Found `{}` in params. Will use it instead of argument".format(alias))
/anaconda3/envs/home_credit_default_risk_competition/lib/python3.5/site-packages/lightgbm/engine.py:99: UserWarning: Found `num_iterations` in params. Will use it instead of argument
  warnings.warn("Found `{}` in params. Will use it instead of argument".format(alias))
/anaconda3/envs/home_credit_default_risk_competition/lib/python3.5/site-packages/lightgbm/engine.py:99: UserWarning: Found `num_iterations` in params. Will use it instead of argument
  warnings.warn("Found `{}` in params. Will use it instead of argument".format(alias))
/anaconda3/envs/home_credit_default_risk_competition/lib/python3.5/site-packages/lightgbm/engine.py:99: UserWarning: Found `num_iterations` in params. Will use it instead of argument
  warnings.warn("Found `{}` in params. Will use it instead of argument".format(alias))
/anaconda3/envs/home_credit_default_risk_competition/lib/python3.5/site-packages/lightgbm/engine.py:99: UserWarning: Found `num_iterations` in params. Will use it instead of argument
  warnings.warn("Found `{}` in params. Will use it instead of argument".format(alias))
/anaconda3/envs/home_credit_default_risk_competition/lib/python3.5/site-packages/lightgbm/engine.py:99: UserWarning: Found `num_iterations` in params. Will use it instead of argument
  warnings.warn("Found `{}` in params. Will use it instead of argument".format(alias))
/anaconda3/envs/home_credit_default_risk_competition/lib/python3.5/site-packages/lightgbm/engine.py:99: UserWarning: Found `num_iterations` in params. Will use it instead of argument
  warnings.warn("Found `{}` in params. Will use it instead of argument".format(alias))
/anaconda3/envs/home_credit_default_risk_competition/lib/python3.5/site-packages/lightgbm/engine.py:99: UserWarning: Found `num_iterations` in params. Will use it instead of argument
  warnings.warn("Found `{}` in params. Will use it instead of argument".format(alias))
/anaconda3/envs/home_credit_default_risk_competition/lib/python3.5/site-packages/lightgbm/engine.py:99: UserWarning: Found `num_iterations` in params. Will use it instead of argument
  warnings.warn("Found `{}` in params. Will use it instead of argument".format(alias))
/anaconda3/envs/home_credit_default_risk_competition/lib/python3.5/site-packages/lightgbm/engine.py:99: UserWarning: Found `num_iterations` in params. Will use it instead of argument
  warnings.warn("Found `{}` in params. Will use it instead of argument".format(alias))
/anaconda3/envs/home_credit_default_risk_competition/lib/python3.5/site-packages/lightgbm/engine.py:99: UserWarning: Found `num_iterations` in params. Will use it instead of argument
  warnings.warn("Found `{}` in params. Will use it instead of argument".format(alias))
/anaconda3/envs/home_credit_default_risk_competition/lib/python3.5/site-packages/lightgbm/engine.py:99: UserWarning: Found `num_iterations` in params. Will use it instead of argument
  warnings.warn("Found `{}` in params. Will use it instead of argument".format(alias))
/anaconda3/envs/home_credit_default_risk_competition/lib/python3.5/site-packages/lightgbm/engine.py:99: UserWarning: Found `num_iterations` in params. Will use it instead of argument
  warnings.warn("Found `{}` in params. Will use it instead of argument".format(alias))
/anaconda3/envs/home_credit_default_risk_competition/lib/python3.5/site-packages/lightgbm/engine.py:99: UserWarning: Found `num_iterations` in params. Will use it instead of argument
  warnings.warn("Found `{}` in params. Will use it instead of argument".format(alias))
/anaconda3/envs/home_credit_default_risk_competition/lib/python3.5/site-packages/lightgbm/engine.py:99: UserWarning: Found `num_iterations` in params. Will use it instead of argument
  warnings.warn("Found `{}` in params. Will use it instead of argument".format(alias))
/anaconda3/envs/home_credit_default_risk_competition/lib/python3.5/site-packages/lightgbm/engine.py:99: UserWarning: Found `num_iterations` in params. Will use it instead of argument
  warnings.warn("Found `{}` in params. Will use it instead of argument".format(alias))
/anaconda3/envs/home_credit_default_risk_competition/lib/python3.5/site-packages/lightgbm/engine.py:99: UserWarning: Found `num_iterations` in params. Will use it instead of argument
  warnings.warn("Found `{}` in params. Will use it instead of argument".format(alias))
/anaconda3/envs/home_credit_default_risk_competition/lib/python3.5/site-packages/lightgbm/engine.py:99: UserWarning: Found `num_iterations` in params. Will use it instead of argument
  warnings.warn("Found `{}` in params. Will use it instead of argument".format(alias))
/anaconda3/envs/home_credit_default_risk_competition/lib/python3.5/site-packages/lightgbm/engine.py:99: UserWarning: Found `num_iterations` in params. Will use it instead of argument
  warnings.warn("Found `{}` in params. Will use it instead of argument".format(alias))
/anaconda3/envs/home_credit_default_risk_competition/lib/python3.5/site-packages/lightgbm/engine.py:99: UserWarning: Found `num_iterations` in params. Will use it instead of argument
  warnings.warn("Found `{}` in params. Will use it instead of argument".format(alias))
/anaconda3/envs/home_credit_default_risk_competition/lib/python3.5/site-packages/lightgbm/engine.py:99: UserWarning: Found `num_iterations` in params. Will use it instead of argument
  warnings.warn("Found `{}` in params. Will use it instead of argument".format(alias))
/anaconda3/envs/home_credit_default_risk_competition/lib/python3.5/site-packages/lightgbm/engine.py:99: UserWarning: Found `num_iterations` in params. Will use it instead of argument
  warnings.warn("Found `{}` in params. Will use it instead of argument".format(alias))
/anaconda3/envs/home_credit_default_risk_competition/lib/python3.5/site-packages/lightgbm/engine.py:99: UserWarning: Found `num_iterations` in params. Will use it instead of argument
  warnings.warn("Found `{}` in params. Will use it instead of argument".format(alias))
/anaconda3/envs/home_credit_default_risk_competition/lib/python3.5/site-packages/lightgbm/engine.py:99: UserWarning: Found `num_iterations` in params. Will use it instead of argument
  warnings.warn("Found `{}` in params. Will use it instead of argument".format(alias))
/anaconda3/envs/home_credit_default_risk_competition/lib/python3.5/site-packages/lightgbm/engine.py:99: UserWarning: Found `num_iterations` in params. Will use it instead of argument
  warnings.warn("Found `{}` in params. Will use it instead of argument".format(alias))
/anaconda3/envs/home_credit_default_risk_competition/lib/python3.5/site-packages/lightgbm/engine.py:99: UserWarning: Found `num_iterations` in params. Will use it instead of argument
  warnings.warn("Found `{}` in params. Will use it instead of argument".format(alias))
/anaconda3/envs/home_credit_default_risk_competition/lib/python3.5/site-packages/lightgbm/engine.py:99: UserWarning: Found `num_iterations` in params. Will use it instead of argument
  warnings.warn("Found `{}` in params. Will use it instead of argument".format(alias))
Best hyperparameter combo:
{'num_leaves': 200, 'num_iterations': 10000, 'learning_rate': 0.1, 'sub_feature': 0.3, 'lambda_l2': 0.1, 'max_depth': -1, 'min_data_in_leaf': 40}
ROC AUC score of best hyperparameter combo:
0.723550734163
In [658]:
print(grid.predict_proba(X_test_final)[:,1])
[  8.54037372e-03   2.34626595e-06   1.15907921e-05 ...,   1.44771135e-06
   2.02137025e-05   4.02830947e-05]
In [655]:
# Use the LightGBM classifier with parameters discovered in GridSearchCV to 
# make predictions on the test validation set. Calculate the area under ROC 
# curve score of these predictions.

# After running GridSearchCV above, I observed that:
# 1. 

# Convert dataframes to LGB format
lgb_training = lgb.Dataset(X_train_final, y_train)

# Final parameters for LightGBM training
params = {}
params['learning_rate'] = 0.001
params['boosting_type'] = 'gbdt'
params['objective'] = 'binary'
params['metric'] = 'auc'
params['num_leaves'] = 200
params['max_depth'] = 20
params['max_bin'] = 110
params['lambda_l2'] = 0.1
params['bagging_freq'] = 1
params['bagging_fraction'] = 0.95
params['bagging_seed'] = 1
params['feature_fraction'] = 0.9
params['feature_fraction_seed'] = 1
params['random_state'] = 42

# Fit the LightGBM classifier to the training data
clf_lgb = lgb.train(params, lgb_training, 15000)

# Classifier's estimates of probability of the positive class (TARGET=1): the 
# probability estimate of each borrower making at least one late loan payment.
lgb_tuned_y_score = clf_lgb.predict(X_test_final)

# The area under the ROC curve between the true target values and the 
# probability estimates of the predicted values.
lgb_tuned_roc_auc_score = roc_auc_scorer(y_test, lgb_tuned_y_score)

# Add the LightGBM classifier's scores to the results list.
#y_score_list.append(lgb_tuned_y_score)
#clf_label_list.append('LightGBM All Features, Further Tuning')

print('LightGBM (All Features, Further Tuning) test validation set predictions\' ROC AUC score: {}'.format(lgb_tuned_roc_auc_score))
LightGBM (All Features, Further Tuning) test validation set predictions' ROC AUC score: 0.7609160310721934
In [698]:
# Plot the 50 largest LightGBM feature importances:
plt.figure(figsize = (34,26), dpi=300)
feat_importances = pd.Series(clf_lgb.feature_importance(importance_type='split', iteration=-1), clf_lgb.feature_name())
feat_importances = feat_importances.nlargest(50).sort_values(axis='index', ascending = True)
feat_importances.plot(kind='barh')
plt.title('LightGBM Top 50 Feature Importances', fontsize=24)
plt.xlabel('Feature Importance Value', fontsize=22)
plt.ylabel('Feature Name', fontsize=22)
plt.xticks(fontsize=20)
plt.yticks(fontsize=20)
plt.savefig('lightGBMFeatureImportances.png')
plt.show()

VII. Results

ROC AUC scores of various classifiers

Unless specifically noted otherwise, each classifier was trained on the 184 binary features and 17 PCA reduced numerical features that comprised all 201 features from the original preprocessed training set.

Certain LightGBM classifiers were trained on various different feature subsets/supersets of this original training set. These classifiers are indicated in the table and plot that follow.

ROC AUC scores indicate performance of classifier probability predictions made for the labels of the test validation set, which is 20% of the size (rows) of the training set contained in the application_train.csv data table:

Classifier Name ROC AUC Score
Naive Bayes (All Features) 0.546645662333944
AdaBoost (All Features) 0.7462758964509755
Logistic Regression (All Features) 0.7471756350178691
Multi-Layer Perceptron (All Features) 0.7429017839300756
LightGBM (All Features) 0.7592132612569703
Naive Bayes (PCA) 0.5452255614331999
AdaBoost (PCA) 0.7415669749755673
Logistic Regression (PCA) 0.743963963781135
Multi-Layer Perceptron (PCA) 0.7439527449175637
LightGBM (PCA) 0.7483887050110797
Naive Bayes (SelectKBest Features, K=30) 0.6748662184461512
AdaBoost (SelectKBest Features, K=30) 0.7330739254581403
Logistic Regression (SelectKBest Features, K=30) 0.7367180213600446
Multi-Layer Perceptron (SelectKBest Features, K=30) 0.7358901049573279
LightGBM (SelectKBest Features, K=30) 0.7394787228642934
LightGBM (All Features, Further Tuning) 0.7609160310721934
In [650]:
# Display ROC curves of the Naive Bayes, AdaBoost, Logistic Regression, and 
# Multi-Layer Perceptron classifiers' probability predictions.
vs.plot_roc_curves(y_test, y_score_list, clf_label_list, title='Receiver Operating Characteristic Curves');

VIII. Submitting to Kaggle

In [651]:
# Train the final LightGBM classifier on the entire training set

# Load the main data tables
application_train_data = pd.read_csv("data/application_train.csv")
application_test_data = pd.read_csv("data/application_test.csv")

# Load the Bureau data table
bureau_data = pd.read_csv("data/bureau.csv")

# 1. Create lists of different feature types in the main data 
# frame, based on how each type will need to be preprocessed.

# i. All 18 categorical features needing one-hot encoding. 
#    Includes the 4 categorical features originally 
#    mis-identified as having been normalized:
#    EMERGENCYSTATE_MODE, HOUSETYPE_MODE, WALLSMATERIAL_MODE, 
#    FONDKAPREMONT_MODE
cat_feat_need_one_hot = [
    'NAME_CONTRACT_TYPE', 'CODE_GENDER', 'FLAG_OWN_CAR', 
    'FLAG_OWN_REALTY', 'NAME_INCOME_TYPE', 'NAME_EDUCATION_TYPE', 
    'NAME_FAMILY_STATUS', 'NAME_HOUSING_TYPE', 'REGION_RATING_CLIENT',
    'REGION_RATING_CLIENT_W_CITY', 'WEEKDAY_APPR_PROCESS_START', 'ORGANIZATION_TYPE',
    'NAME_TYPE_SUITE', 'OCCUPATION_TYPE', 'EMERGENCYSTATE_MODE',
    'HOUSETYPE_MODE', 'WALLSMATERIAL_MODE', 'FONDKAPREMONT_MODE'
]

# ii. All 32 binary categorical features already one-hot encoded.
bin_cat_feat = [
    'FLAG_MOBIL', 'FLAG_EMP_PHONE', 'FLAG_WORK_PHONE',
    'FLAG_CONT_MOBILE', 'FLAG_PHONE', 'FLAG_EMAIL',
    'REG_REGION_NOT_LIVE_REGION', 'REG_REGION_NOT_WORK_REGION', 'LIVE_REGION_NOT_WORK_REGION',
    'REG_CITY_NOT_LIVE_CITY', 'REG_CITY_NOT_WORK_CITY', 'LIVE_CITY_NOT_WORK_CITY',
    'FLAG_DOCUMENT_2', 'FLAG_DOCUMENT_3', 'FLAG_DOCUMENT_4',
    'FLAG_DOCUMENT_5', 'FLAG_DOCUMENT_6', 'FLAG_DOCUMENT_7',
    'FLAG_DOCUMENT_8', 'FLAG_DOCUMENT_9', 'FLAG_DOCUMENT_10',
    'FLAG_DOCUMENT_11', 'FLAG_DOCUMENT_12', 'FLAG_DOCUMENT_13',
    'FLAG_DOCUMENT_14', 'FLAG_DOCUMENT_15', 'FLAG_DOCUMENT_16',
    'FLAG_DOCUMENT_17', 'FLAG_DOCUMENT_18', 'FLAG_DOCUMENT_19',
    'FLAG_DOCUMENT_20', 'FLAG_DOCUMENT_21'
]

# iii. All 2 non-normalized numerical features with skewed distributions 
#    and negative values. These features will need to have their 
#    distributions translated to positive ranges before being 
#    log-transformed, and then later scaled to the range [0,1].
non_norm_feat_neg_values_skewed = [
    'DAYS_REGISTRATION', 'DAYS_LAST_PHONE_CHANGE'
]


# iv. All 15 non-normalized numerical features with skewed distributions, 
#    and only positive values. These features will need to be 
#    log-transformed, and eventually scaled to the range [0,1].
non_norm_feat_pos_values_skewed = [
    'AMT_INCOME_TOTAL', 'AMT_CREDIT', 'AMT_ANNUITY',
    'AMT_GOODS_PRICE', 'OBS_30_CNT_SOCIAL_CIRCLE', 'DEF_30_CNT_SOCIAL_CIRCLE',
    'OBS_60_CNT_SOCIAL_CIRCLE', 'DEF_60_CNT_SOCIAL_CIRCLE', 'AMT_REQ_CREDIT_BUREAU_HOUR',
    'AMT_REQ_CREDIT_BUREAU_DAY', 'AMT_REQ_CREDIT_BUREAU_WEEK', 'AMT_REQ_CREDIT_BUREAU_MON',
    'AMT_REQ_CREDIT_BUREAU_QRT', 'AMT_REQ_CREDIT_BUREAU_YEAR', 'OWN_CAR_AGE'
]

# v. All 4 numerical features with normal shapes but needing to be scaled 
#    to the range [0,1].
norm_feat_need_scaling = [
    'DAYS_BIRTH', 'DAYS_ID_PUBLISH', 'HOUR_APPR_PROCESS_START',
    'REGION_POPULATION_RELATIVE'
]

# vi. All 46 numerical features that have been normalized to the range 
#   [0,1]. These features will need neither log-transformation, nor 
#   any further scaling.
norm_feat_not_need_scaling = [
    'EXT_SOURCE_2', 'EXT_SOURCE_3', 'YEARS_BEGINEXPLUATATION_AVG',
    'YEARS_BEGINEXPLUATATION_MODE', 'YEARS_BEGINEXPLUATATION_MEDI', 'FLOORSMAX_AVG', 
    'FLOORSMAX_MODE', 'FLOORSMAX_MEDI', 'LIVINGAREA_AVG',
    'LIVINGAREA_MODE', 'LIVINGAREA_MEDI', 'ENTRANCES_AVG',
    'ENTRANCES_MODE', 'ENTRANCES_MEDI', 'APARTMENTS_AVG',
    'APARTMENTS_MODE', 'APARTMENTS_MEDI', 'ELEVATORS_AVG', 
    'ELEVATORS_MODE', 'ELEVATORS_MEDI', 'NONLIVINGAREA_AVG',
    'NONLIVINGAREA_MODE', 'NONLIVINGAREA_MEDI', 'EXT_SOURCE_1',
    'BASEMENTAREA_AVG', 'BASEMENTAREA_MODE', 'BASEMENTAREA_MEDI',
    'LANDAREA_AVG', 'LANDAREA_MODE', 'LANDAREA_MEDI',
    'YEARS_BUILD_AVG', 'YEARS_BUILD_MODE', 'YEARS_BUILD_MEDI',
    'FLOORSMIN_AVG', 'FLOORSMIN_MODE', 'FLOORSMIN_MEDI',
    'LIVINGAPARTMENTS_AVG', 'LIVINGAPARTMENTS_MODE', 'LIVINGAPARTMENTS_MEDI',
    'NONLIVINGAPARTMENTS_AVG', 'NONLIVINGAPARTMENTS_MODE', 'NONLIVINGAPARTMENTS_MEDI',
    'COMMONAREA_AVG', 'COMMONAREA_MODE', 'COMMONAREA_MEDI',
    'TOTALAREA_MODE'
]

# vii. The remaining 3 features in the main data frame that will be 
#    re-engineered and transformed into different features
feat_to_be_reengineered = [
    'CNT_CHILDREN', 'CNT_FAM_MEMBERS', 'DAYS_EMPLOYED'
]

# 2. Separate target data from training dataset.
targets = application_train_data['TARGET']
features_raw = application_train_data.drop('TARGET', axis = 1)

# 3. Because the entire training set from the file application_train.csv 
# is being used for training, there is no need at this point to do a 
# train test validation split.
y_train = targets
X_train_raw = features_raw

# 4. Use the CNT_CHILDREN feature to engineer a binary 
# categorical feature called HAS_CHILDREN. If value of CNT_CHILDREN is 
# greater than 0, the value of HAS_CHILDREN will be 1. If value of CNT_CHILDREN is 
# 0, value of HAS_CHILDREN will be 0. 
CNT_CHILDREN_train = X_train_raw['CNT_CHILDREN']
HAS_CHILDREN = CNT_CHILDREN_train.map(lambda x: 1 if x > 0 else 0)

# Append the newly engineered HAS_CHILDREN feature to the main dataframe.
X_train_raw = X_train_raw.assign(HAS_CHILDREN=HAS_CHILDREN.values)

# 5. Drop the CNT_CHILDREN column from the main dataframe
X_train_raw = X_train_raw.drop('CNT_CHILDREN', axis=1)

# Add the new HAS_CHILDREN feature to the list of binary categorical 
# features that are already one-hot encoded. There are now 33 such features.
bin_cat_feat = bin_cat_feat + ['HAS_CHILDREN']

# 6. Use the CNT_FAM_MEMBERS feature to engineer a categorical feature called NUMBER_FAMILY_MEMBERS. 
# If CNT_FAM_MEMBERS is 1.0, then the value of NUMBER_FAMILY_MEMBERS will be 'one'. If CNT_FAM_MEMBERS is 2.0, 
# then NUMBER_FAMILY_MEMBERS will be 'two'. If CNT_FAM_MEMBERS is 3.0 or greater, then NUMBER_FAMILY_MEMBERS will 
# be 'three_plus'.
CNT_FAM_MEMBERS_train = X_train_raw['CNT_FAM_MEMBERS']
NUMBER_FAMILY_MEMBERS = CNT_FAM_MEMBERS_train.map(lambda x: 'one' if x == 1 else ('two' if x == 2 else 'three_plus'))

# Append the newly engineered FAMILY_SIZE feature to the main dataframe.
X_train_raw = X_train_raw.assign(NUMBER_FAMILY_MEMBERS=NUMBER_FAMILY_MEMBERS.values)

# 7. Drop the CNT_FAM_MEMBERS feature from the main dataframe
X_train_raw = X_train_raw.drop('CNT_FAM_MEMBERS', axis=1)

# Add the new NUMBER_FAMILY_MEMBERS feature to the list of categorical 
# features that will need to be one-hot encoded. There are now 19 of these features.
cat_feat_need_one_hot = cat_feat_need_one_hot + ['NUMBER_FAMILY_MEMBERS']

# 8. Use the CREDIT_DAY_OVERDUE feature in bureau.csv to engineer the binary 
# categorical HAS_CREDIT_BUREAU_LOANS_OVERDUE feature. If CREDIT_DAY_OVERDUE for a 
# particular borrower ID (SK_ID_CURR) is greater than 0, then the value of 
# HAS_CREDIT_BUREAU_LOANS_OVERDUE will be 1. If CREDIT_DAY_OVERDUE for a particular 
# borrower ID is 0, then the value of HAS_CREDIT_BUREAU_LOANS_OVERDUE will be 0.

# Filter the bureau data table for loans which are overdue (have a value 
# for CREDIT_DAY_OVERDUE that's greater than 0)
bureau_data_filtered_for_overdue = bureau_data[bureau_data['CREDIT_DAY_OVERDUE'] > 0]

def build_feature_HAS_CREDIT_BUREAU_LOANS_OVERDUE(dataframe):
    """
    Use the CREDIT_DAY_OVERDUE feature in bureau.csv to engineer the binary 
    categorical HAS_CREDIT_BUREAU_LOANS_OVERDUE feature. If CREDIT_DAY_OVERDUE for a 
    particular borrower ID (SK_ID_CURR) is greater than 0, then the value of 
    HAS_CREDIT_BUREAU_LOANS_OVERDUE will be 1. If CREDIT_DAY_OVERDUE for a particular 
    borrower ID is 0, then the value of HAS_CREDIT_BUREAU_LOANS_OVERDUE will be 0.

    Parameters:
        dataframe: Pandas dataframe containing a training or testing dataset
        
    Returns: The dataframe with HAS_CREDIT_BUREAU_LOANS_OVERDUE feature appended to it.
    """
    # Create a series called HAS_CREDIT_BUREAU_LOANS_OVERDUE and fill it with zeros.  
    # Its index is identical to that of the main dataframe. It will eventually be appended 
    # to the main data frame as a column.
    HAS_CREDIT_BUREAU_LOANS_OVERDUE = pd.Series(data=0, index = dataframe['SK_ID_CURR'].index)

    # A list of all the borrowers IDs in the main dataframe
    main_data_table_borrower_IDs = dataframe['SK_ID_CURR'].values

    # For each loan in the bureau data table that is overdue 
    # (has a value for CREDIT_DAY_OVERDUE that's greater than 0)
    for index, row in bureau_data_filtered_for_overdue.iterrows():
        # The borrower ID (SK_ID_CURR) that owns the overdue loan
        borrower_ID = row['SK_ID_CURR']
        # If the borrower ID owning the overdue loan is also 
        # in the main data frame, then enter a value of 1 in 
        # the series HAS_CREDIT_BUREAU_LOANS_OVERDUE at an index 
        # that is identical to the index of the borrower ID 
        # in the main data frame.
        if borrower_ID in main_data_table_borrower_IDs:
            # The index of the borrower's row in the main data table.
            borrower_index_main_data_table = dataframe.index[dataframe['SK_ID_CURR'] == borrower_ID].tolist()[0]
            # Place a value of 1 at the index of the series HAS_CREDIT_BUREAU_LOANS_OVERDUE 
            # which corresponds to the index of the borrower's ID in the main data table.
            HAS_CREDIT_BUREAU_LOANS_OVERDUE.loc[borrower_index_main_data_table] = 1
    # Append the newly engineered HAS_CREDIT_BUREAU_LOANS_OVERDUE feature to the main dataframe.
    dataframe = dataframe.assign(HAS_CREDIT_BUREAU_LOANS_OVERDUE=HAS_CREDIT_BUREAU_LOANS_OVERDUE.values)
    return dataframe

# Build the HAS_CREDIT_BUREAU_LOANS_OVERDUE feature
X_train_raw = build_feature_HAS_CREDIT_BUREAU_LOANS_OVERDUE(X_train_raw)

# Add the new HAS_CREDIT_BUREAU_LOANS_OVERDUE feature to the list of binary categorical 
# features. There are now 34 of these features.
bin_cat_feat = bin_cat_feat + ['HAS_CREDIT_BUREAU_LOANS_OVERDUE']

# 9. Use the DAYS_EMPLOYED feature to engineer a binary categorical feature called HAS_JOB. 
# If the value of DAYS_EMPLOYED is 0 or less, then HAS_JOB will be 1. Otherwise, HAS_JOB will 
# be 0. This condition will apply to all borrowers who had a value of 365243 for DAYS_EMPLOYED, 
# which I hypothesized can be best interpreted as meaning that a borrower does not have a job.
DAYS_EMPLOYED_train = X_train_raw['DAYS_EMPLOYED']
HAS_JOB = DAYS_EMPLOYED_train.map(lambda x: 1 if x <= 0 else 0)

# Append the newly engineered FAMILY_SIZE feature to the main dataframe.
X_train_raw = X_train_raw.assign(HAS_JOB=HAS_JOB.values)

# 10. Drop the DAYS_EMPLOYED feature from the main dataframe
X_train_raw = X_train_raw.drop('DAYS_EMPLOYED', axis=1)

# Add the new HAS_JOB feature to the list of binary categorical features. 
# There are now 35 of these features.
bin_cat_feat = bin_cat_feat + ['HAS_JOB']

# 11. Translate the 2 non-normalized numerical features that have skewed distributions 
# and negative values: DAYS_REGISTRATION, and DAYS_LAST_PHONE_CHANGE

def translate_negative_valued_features(dataframe, feature_name_list):
    """
    Translate a dataset's continuous features containing several negative 
    values. The dataframe is modified such that all values of each feature 
    listed in the feature_name_list parameter become positive.

    Parameters:
        dataframe: Pandas dataframe containing the features
        feature_name_list: List of strings, containing the names 
                           of each feature whose values will be 
                           translated
    """
    for feature in feature_name_list:
        # The minimum, most-negative, value of the feature
        feature_min_value = dataframe[feature].min()
        # Translate each value of the feature in a positive direction, 
        # of magnitude that's equal to the feature's most negative value.
        dataframe[feature] = dataframe[feature].apply(lambda x: x - feature_min_value)

# Translate the above two negatively-valued features to positive values
translate_negative_valued_features(X_train_raw, non_norm_feat_neg_values_skewed)

# 12. Log-transform all 17 non-normalized numerical features that have skewed distributions. 
# These 17 features include the 2 that were translated to positive ranges in Step 11.

# Add the 2 features translated to positive ranges above in Step 11 to 
# the list of non-normalized skewed features with positive values. This is 
# the set of features that will be log-transformed
log_transform_feats = non_norm_feat_pos_values_skewed + non_norm_feat_neg_values_skewed

X_train_raw[log_transform_feats] = X_train_raw[log_transform_feats].apply(lambda x: np.log(x + 1))

# 13. Replace 'NaN' values for all numerical features with each feature's mean. Fit an imputer 
# to each numerical feature containing at least one 'NaN' entry.

# Create a list of all the 67 numerical features in the main dataframe. These include all 
# 17 features that were log-transformed in Step 12, as well as the 4 normal features that 
# still need to be scaled, as well as the 46 normal features that don't need scaling.
numerical_features = log_transform_feats + norm_feat_need_scaling + norm_feat_not_need_scaling

# Create a list of all numerical features in the training set that have at least one 'NaN' entry
numerical_features_with_nan = X_train_raw[numerical_features].columns[X_train_raw[numerical_features].isna().any()].tolist()

# Create an imputer
imputer = Imputer()
# Fit the imputer to each numerical feature in the training set that has 'NaN' values, 
# and replace each 'NaN' entry of each feature with that feature's mean.
X_train_raw[numerical_features_with_nan] = imputer.fit_transform(X_train_raw[numerical_features_with_nan])

# 14. Remove the borrower ID column, SK_ID_CURR, from the main dataframe
X_train_raw = X_train_raw.drop('SK_ID_CURR', axis=1)

# 15. One-hot encode all 19 non-binary categorical features.
X_train_raw = pd.get_dummies(X_train_raw, columns=cat_feat_need_one_hot)

# Create a list that includes only the newly one-hot encoded features 
# as well as all the categorical features that were already binary.
all_bin_cat_feat = X_train_raw.columns.tolist()
for column_name in X_train_raw[numerical_features].columns.tolist():
    all_bin_cat_feat.remove(column_name)

# 16. Replace all 'NaN' values in all binary categorical features with 0.

# Create a list of binary categorical features with at least one 'NaN' entry
bin_cat_feat_with_nan = X_train_raw[all_bin_cat_feat].columns[X_train_raw[all_bin_cat_feat].isna().any()].tolist()

# Replace each 'NaN' value in each of these binary features with 0
X_train_raw[bin_cat_feat_with_nan] = X_train_raw[bin_cat_feat_with_nan].fillna(value=0)

# 17. Fit a min-max scaler to each of the 17 log-transformed numerical features, as well 
# as to the 4 features DAYS_BIRTH, DAYS_ID_PUBLISH, HOUR_APPR_PROCESS_START, and the normalized 
# feature REGION_POPULATION_RELATIVE. Each feature will be scaled to a range [0.0, 1.0].

# Build a list of all 21 features needing scaling. Add the list of features that 
# were log-normalized above in Step 12 to the list of normally shaped features 
# that need to be scaled to the range [0,1].
feats_to_scale = norm_feat_need_scaling + log_transform_feats

# Initialize a scaler with the default range of [0,1]
scaler = MinMaxScaler()

# Fit the scaler to each of the features of the train set that need to be scaled, 
# then transform each of these features' values to the new scale.
X_train_raw[feats_to_scale] = scaler.fit_transform(X_train_raw[feats_to_scale])

# Rename the dataframe to indicate that its columns have been fully preprocessed.
X_train_final = X_train_raw

print('Entire training dataset preprocessing complete.')
print('Number of columns: {}. Expected: 251.'.format(X_train_final.shape[1]))
print('Number of rows: {}. Expected: 307511.'.format(X_train_final.shape[0]))
print('Number of labels: {}. Expected: 307511.'.format(y_train.shape[0]))
Entire training dataset preprocessing complete.
Number of columns: 251. Expected: 251.
Number of rows: 307511. Expected: 307511.
Number of labels: 307511. Expected: 307511.
In [652]:
# Fit a LightGBM classifier to the entire training set using the parameters 
# that were tuned in the final refinement step above.

# Convert the entire training dataframe to LGB format
lgb_training = lgb.Dataset(X_train_final, y_train)

# Final parameters for LightGBM training
params = {}
params['learning_rate'] = 0.001
params['boosting_type'] = 'gbdt'
params['objective'] = 'binary'
params['metric'] = 'auc'
params['num_leaves'] = 200
params['max_depth'] = 20
params['max_bin'] = 110
params['lambda_l2'] = 0.1
params['bagging_freq'] = 1
params['bagging_fraction'] = 0.95
params['bagging_seed'] = 1
params['feature_fraction'] = 0.9
params['feature_fraction_seed'] = 1
params['random_state'] = 42

# Fit the LightGBM classifier to the training data
clf_lgb = lgb.train(params, lgb_training, 15000)
In [653]:
# Build a prediction pipeline for the testing data table (application_test.csv) that 
# saves prediction probabilities to a CSV file, which will then be submitted on Kaggle.

def testing_data_table_predictions_to_csv(clf, testing_data_table, isLightGBM):
    """
    A prediction pipeline that:
    1. Preprocesses the 48,744 row testing data table 
    2. Uses a classifier to compute estimates of the probability of the positive 
       class (TARGET=1) for each borrower: the probability estimate of each borrower 
       making at least one late loan payment.
    3. Saves a CSV file that contains probabilities of target labels for each 
       borrower (SK_ID_CURR) in the testing data table.
    4. isLightGBM: Boolean, a flag that indicates whether or not the classifier is 
                   LightGBM. If True, 

    Parameters:
        clf: A machine learning classifier object that has already been fit to 
                    the training data.
        testing_data_table: Pandas dataframe containing the testing dataset.
    """
    
    # Get a list of the borrower IDs (SK_ID_CURR column). The borrower ID must be 
    # placed in each row of CSV file that will be created.
    borrower_IDs = testing_data_table['SK_ID_CURR']
    
    # Preprocess the testing data table so that predictions can be made on it.
    X_test_final = test_set_preprocessing_pipeline(testing_data_table)
    #print('application_test.csv testing set processing complete. The processed dataframe now has {} columns. Expected: 251.'.format(X_test_final.shape[1]))
    
    # Classifier's estimates of probability of the positive class (TARGET=1): the 
    # probability estimate of each borrower making at least one late loan payment. 
    # If classifier is LightGBM, the method for making predictions is merely 'predict'
    # and the arrray containing these probabilities has slightly different shape than 
    # those produced by the other classifiers.
    if isLightGBM:
        clf_y_score = clf.predict(X_test_final)
    else:
        clf_y_score = clf.predict_proba(X_test_final)[:, 1]
    
    # Create the CSV file that will be saved
    file_output = 'dellinger_kaggle_home_credit_submission5.csv'
    # Write to the CSV file
    with open(file_output, 'w') as csvfile:
        writer = csv.writer(csvfile)
        # Write the header row
        writer.writerow(['SK_ID_CURR','TARGET'])
        # Write a row for each borrower that contains the 
        # prediction probability of their label.
        for index, value in borrower_IDs.iteritems():
            writer.writerow([value, clf_y_score[index]])

# To submit to Kaggle: the LightGBM Classifier's predictions on full featureset.
# Create predictions on the data in the testing data table (application_test.csv) 
# using the LightGBM classifier fit above. Also create a CSV 
# file containing the prediction probabilities for each borrower ID (SK_ID_CURR) 
# in the testing data table.
testing_data_table_predictions_to_csv(clf_lgb, application_test_data, True)